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Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation | Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, Minghua Deng | Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared "known" samples and private "unknown" samples. Previous works rarely explore the intrinsic geometrical relationship between the two domains, and they manually set a threshold for the overconfident closed-world classifier to reject "unknown" samples. Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues. Specifically, we first develop a random walk-based anchor mining strategy together with a high-order attention mechanism to build correspondence across domains. Then a global joint local domain alignment paradigm is designed, i.e., geometric adversarial learning for global distribution calibration and subgraph-level contrastive learning for local region aggregation. Toward accurate target private samples detection, GATE introduces a universal incremental classifier by modeling the energy uncertainty. We further efficiently generate novel categories by manifold mixup, and minimize the open-set entropy to learn the "unknown" threshold adaptively. Extensive experiments on three benchmarks demonstrate that GATE significantly outperforms previous state-of-the-art UniDA methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Geometric_Anchor_Correspondence_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.html | CVPR 2022 | null |
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation | Ruihuang Li, Shuai Li, Chenhang He, Yabin Zhang, Xu Jia, Lei Zhang | Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training. However, the produced pseudo labels often contain much noise because the model is biased to source domain as well as majority categories. To address the above issues, we propose to directly explore the intrinsic pixel distributions of target domain data, instead of heavily relying on the source domain. Specifically, we simultaneously cluster pixels and rectify pseudo labels with the obtained cluster assignments. This process is done in an online fashion so that pseudo labels could co-evolve with the segmentation model without extra training rounds. To overcome the class imbalance problem on long-tailed categories, we employ a distribution alignment technique to enforce the marginal class distribution of cluster assignments to be close to that of pseudo labels. The proposed method, namely Class-balanced Pixel-level Self-Labeling (CPSL), improves the segmentation performance on target domain over state-of-the-arts by a large margin, especially on long-tailed categories. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Class-Balanced_Pixel-Level_Self-Labeling_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Class-Balanced_Pixel-Level_Self-Labeling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Class-Balanced_Pixel-Level_Self-Labeling_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Class-Balanced_Pixel-Level_Self-Labeling_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Coopernaut: End-to-End Driving With Cooperative Perception for Networked Vehicles | Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu | Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop AutoCastSim, a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate over egocentric driving models in these challenging driving situations and a 5 times smaller bandwidth requirement than prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at https://ut-austin-rpl.github.io/Coopernaut/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cui_Coopernaut_End-to-End_Driving_With_Cooperative_Perception_for_Networked_Vehicles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cui_Coopernaut_End-to-End_Driving_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cui_Coopernaut_End-to-End_Driving_With_Cooperative_Perception_for_Networked_Vehicles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cui_Coopernaut_End-to-End_Driving_With_Cooperative_Perception_for_Networked_Vehicles_CVPR_2022_paper.html | CVPR 2022 | null |
Condensing CNNs With Partial Differential Equations | Anil Kag, Venkatesh Saligrama | Convolutional neural networks (CNNs) rely on the depth of the architecture to obtain complex features. It results in computationally expensive models for low-resource IoT devices. Convolutional operators are local and restricted in the receptive field, which increases with depth. We explore partial differential equations (PDEs) that offer a global receptive field without the added overhead of maintaining large kernel convolutional filters. We propose a new feature layer, called the Global layer, that enforces PDE constraints on the feature maps, resulting in rich features. These constraints are solved by embedding iterative schemes in the network. The proposed layer can be embedded in any deep CNN to transform it into a shallower network. Thus, resulting in compact and computationally efficient architectures achieving similar performance as the original network. Our experimental evaluation demonstrates that architectures with global layers require 2-5xless computational and storage budget without any significant loss in performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kag_Condensing_CNNs_With_Partial_Differential_Equations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kag_Condensing_CNNs_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kag_Condensing_CNNs_With_Partial_Differential_Equations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kag_Condensing_CNNs_With_Partial_Differential_Equations_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Keypoint Detection With Uncertainty Learning for Unseen Species | Changsheng Lu, Piotr Koniusz | Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their heatmaps, tailored to specific body parts, cannot recognize novel keypoints (keypoints not labelled for training) on unseen species. We raise an interesting yet challenging question: how to detect both base (annotated for training) and novel keypoints for unseen species given a few annotated samples? Thus, we propose a versatile Few-shot Keypoint Detection (FSKD) pipeline, which can detect a varying number of keypoints of different kinds. Our FSKD provides the uncertainty estimation of predicted keypoints. Specifically, FSKD involves main and auxiliary keypoint representation learning, similarity learning, and keypoint localization with uncertainty modeling to tackle the localization noise. Moreover, we model the uncertainty across groups of keypoints by multivariate Gaussian distribution to exploit implicit correlations between neighboring keypoints. We show the effectiveness of our FSKD on (i) novel keypoint detection for unseen species, and (ii) few-shot Fine-Grained Visual Recognition (FGVR) and (iii) Semantic Alignment (SA) downstream tasks. For FGVR, detected keypoints improve the classification accuracy. For SA, we showcase a novel thin-plate-spline warping that uses estimated keypoint uncertainty under imperfect keypoint corespondences. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Few-Shot_Keypoint_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.06183 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching | Ozan Özdenizci, Robert Legenstein | Deep neural networks (DNNs) have been shown to be vulnerable against adversarial weight bit-flip attacks through hardware-induced fault-injection methods on the memory systems where network parameters are stored. Recent attacks pose the further concerning threat of finding minimal targeted and stealthy weight bit-flips that preserve expected behavior for untargeted test samples. This renders the attack undetectable from a DNN operation perspective. We propose a DNN defense mechanism to improve robustness in such realistic stealthy weight bit-flip attack scenarios. Our output code matching networks use an output coding scheme where the usual one-hot encoding of classes is replaced by partially overlapping bit strings. We show that this encoding significantly reduces attack stealthiness. Importantly, our approach is compatible with existing defenses and DNN architectures. It can be efficiently implemented on pre-trained models by simply re-defining the output classification layer and finetuning. Experimental benchmark evaluations show that output code matching is superior to existing regularized weight quantization based defenses, and an effective defense against stealthy weight bit-flip attacks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ozdenizci_Improving_Robustness_Against_Stealthy_Weight_Bit-Flip_Attacks_by_Output_Code_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ozdenizci_Improving_Robustness_Against_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ozdenizci_Improving_Robustness_Against_Stealthy_Weight_Bit-Flip_Attacks_by_Output_Code_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ozdenizci_Improving_Robustness_Against_Stealthy_Weight_Bit-Flip_Attacks_by_Output_Code_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Hierarchical Semantic Segmentation With Multiview Cosegmentation and Clustering Transformers | Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo, Xudong Wang, Stella X. Yu | Unsupervised semantic segmentation aims to discover groupings within and across images that capture object- and view-invariance of a category without external supervision. Grouping naturally has levels of granularity, creating ambiguity in unsupervised segmentation. Existing methods avoid this ambiguity and treat it as a factor outside modeling, whereas we embrace it and desire hierarchical grouping consistency for unsupervised segmentation. We approach unsupervised segmentation as a pixel-wise feature learning problem. Our idea is that a good representation must be able to reveal not just a particular level of grouping, but any level of grouping in a consistent and predictable manner across different levels of granularity. We enforce spatial consistency of grouping and bootstrap feature learning with co-segmentation among multiple views of the same image, and enforce semantic consistency across the grouping hierarchy with clustering transformers. We deliver the first data-driven unsupervised hierarchical semantic segmentation method called Hierarchical Segment Grouping (HSG). Capturing visual similarity and statistical co-occurrences, HSG also outperforms existing unsupervised segmentation methods by a large margin on five major object- and scene-centric benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ke_Unsupervised_Hierarchical_Semantic_Segmentation_With_Multiview_Cosegmentation_and_Clustering_Transformers_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.11432 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ke_Unsupervised_Hierarchical_Semantic_Segmentation_With_Multiview_Cosegmentation_and_Clustering_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ke_Unsupervised_Hierarchical_Semantic_Segmentation_With_Multiview_Cosegmentation_and_Clustering_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection | Junyu Luo, Jiahui Fu, Xianghao Kong, Chen Gao, Haibing Ren, Hao Shen, Huaxia Xia, Si Liu | 3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description. Previous methods mostly follow a two-stage paradigm, i.e., language-irrelevant detection and cross-modal matching, which is limited by the isolated architecture. In such a paradigm, the detector needs to sample keypoints from raw point clouds due to the inherent properties of 3D point clouds (irregular and large-scale), to generate the corresponding object proposal for each keypoint. However, sparse proposals may leave out the target in detection, while dense proposals may confuse the matching model. Moreover, the language-irrelevant detection stage can only sample a small proportion of keypoints on the target, deteriorating the target prediction. In this paper, we propose a 3D Single-Stage Referred Point Progressive Selection (3D-SPS) method, which progressively selects keypoints with the guidance of language and directly locates the target. Specifically, we propose a Description-aware Keypoint Sampling (DKS) module to coarsely focus on the points of language-relevant objects, which are significant clues for grounding. Besides, we devise a Target-oriented Progressive Mining (TPM) module to finely concentrate on the points of the target, which is enabled by progressive intra-modal relation modeling and inter-modal target mining. 3D-SPS bridges the gap between detection and matching in the 3D visual grounding task, localizing the target at a single stage. Experiments demonstrate that 3D-SPS achieves state-of-the-art performance on both ScanRefer and Nr3D/Sr3D datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_3D-SPS_Single-Stage_3D_Visual_Grounding_via_Referred_Point_Progressive_Selection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_3D-SPS_Single-Stage_3D_Visual_Grounding_via_Referred_Point_Progressive_Selection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_3D-SPS_Single-Stage_3D_Visual_Grounding_via_Referred_Point_Progressive_Selection_CVPR_2022_paper.html | CVPR 2022 | null |
TubeR: Tubelet Transformer for Video Action Detection | Jiaojiao Zhao, Yanyi Zhang, Xinyu Li, Hao Chen, Bing Shuai, Mingze Xu, Chunhui Liu, Kaustav Kundu, Yuanjun Xiong, Davide Modolo, Ivan Marsic, Cees G. M. Snoek, Joseph Tighe | We propose TubeR: a simple solution for spatio-temporal video action detection. Different from existing methods that depend on either an off-line actor detector or hand-designed actor-positional hypotheses like proposals or anchors, we propose to directly detect an action tubelet in video by simultaneously performing action localization and recognition from a single representation. TubeR learns a set of tubelet-queries and utilizes a tubelet-attention module to model the dynamic spatio-temporal nature of a video clip, which effectively reinforces the model capacity compared to using actor-positional hypotheses in the spatio-temporal space. For videos containing transitional states or scene changes, we propose a context aware classification head to utilize short-term and long-term context to strengthen action classification, and an action switch regression head for detecting the precise temporal action extent. TubeR directly produces action tubelets with variable lengths and even maintains good results for long video clips. TubeR outperforms the previous state-of-the-art on commonly used action detection datasets AVA, UCF101-24 and JHMDB51-21. Code will be available on GluonCV(https://cv.gluon.ai/). | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_TubeR_Tubelet_Transformer_for_Video_Action_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_TubeR_Tubelet_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.00969 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_TubeR_Tubelet_Transformer_for_Video_Action_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_TubeR_Tubelet_Transformer_for_Video_Action_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
LASER: LAtent SpacE Rendering for 2D Visual Localization | Zhixiang Min, Naji Khosravan, Zachary Bessinger, Manjunath Narayana, Sing Bing Kang, Enrique Dunn, Ivaylo Boyadzhiev | We present LASER, an image-based Monte Carlo Localization (MCL) framework for 2D floor maps. LASER introduces the concept of latent space rendering, where 2D pose hypotheses on the floor map are directly rendered into a geometrically-structured latent space by aggregating viewing ray features. Through a tightly coupled rendering codebook scheme, the viewing ray features are dynamically determined at rendering-time based on their geometries (i.e. length, incident-angle), endowing our representation with view-dependent fine-grain variability. Our codebook scheme effectively disentangles feature encoding from rendering, allowing the latent space rendering to run at speeds above 10KHz. Moreover, through metric learning, our geometrically-structured latent space is common to both pose hypotheses and query images with arbitrary field of views. As a result, LASER achieves state-of-the-art performance on large-scale indoor localization datasets (i.e. ZInD and Structured3D) for both panorama and perspective image queries, while significantly outperforming existing learning-based methods in speed. | https://openaccess.thecvf.com/content/CVPR2022/papers/Min_LASER_LAtent_SpacE_Rendering_for_2D_Visual_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Min_LASER_LAtent_SpacE_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Min_LASER_LAtent_SpacE_Rendering_for_2D_Visual_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Min_LASER_LAtent_SpacE_Rendering_for_2D_Visual_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection | JongMok Kim, JooYoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak | Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student network by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information. Especially when extending SSL to semi-supervised object detection (SSOD), many strong augmentation methodologies related to image geometry and interpolation-regularization are hard to utilize since they possibly hurt the location information of the bounding box in the object detection task. To address this, we introduce a simple yet effective data augmentation method, Mix/UnMix (MUM), which unmixes feature tiles for the mixed image tiles for the SSOD framework. Our proposed method makes mixed input image tiles and reconstructs them in the feature space. Thus, MUM can enjoy the interpolation-regularization effect from non-interpolated pseudo-labels and successfully generate a meaningful weak-strong pair. Furthermore, MUM can be easily equipped on top of various SSOD methods. Extensive experiments on MS-COCO and PASCAL VOC datasets demonstrate the superiority of MUM by consistently improving the mAP performance over the baseline in all the tested SSOD benchmark protocols. The code is released at https://github.com/JongMokKim/mix-unmix. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_MUM_Mix_Image_Tiles_and_UnMix_Feature_Tiles_for_Semi-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_MUM_Mix_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.10958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_MUM_Mix_Image_Tiles_and_UnMix_Feature_Tiles_for_Semi-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_MUM_Mix_Image_Tiles_and_UnMix_Feature_Tiles_for_Semi-Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles | Qingzhao Zhang, Shengtuo Hu, Jiachen Sun, Qi Alfred Chen, Z. Morley Mao | Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness of trajectory prediction or investigated whether the worst-case prediction can still lead to safe planning. To bridge this gap, we study the adversarial robustness of trajectory prediction models by proposing a new adversarial attack that perturbs normal vehicle trajectories to maximize the prediction error. Our experiments on three models and three datasets show that the adversarial prediction increases the prediction error by more than 150%. Our case studies show that if an adversary drives a vehicle close to the target AV following the adversarial trajectory, the AV may make an inaccurate prediction and even make unsafe driving decisions. We also explore possible mitigation techniques via data augmentation and trajectory smoothing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_On_Adversarial_Robustness_of_Trajectory_Prediction_for_Autonomous_Vehicles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_On_Adversarial_Robustness_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.05057 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_On_Adversarial_Robustness_of_Trajectory_Prediction_for_Autonomous_Vehicles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_On_Adversarial_Robustness_of_Trajectory_Prediction_for_Autonomous_Vehicles_CVPR_2022_paper.html | CVPR 2022 | null |
Kubric: A Scalable Dataset Generator | Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti (Derek) Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi | Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 11 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification. | https://openaccess.thecvf.com/content/CVPR2022/papers/Greff_Kubric_A_Scalable_Dataset_Generator_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Greff_Kubric_A_Scalable_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03570 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Greff_Kubric_A_Scalable_Dataset_Generator_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Greff_Kubric_A_Scalable_Dataset_Generator_CVPR_2022_paper.html | CVPR 2022 | null |
Unpaired Deep Image Deraining Using Dual Contrastive Learning | Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, Zhentao Fan | Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible. However, without the paired data as the supervision, learning a SID network is challenging. Moreover, simply using existing unpaired learning methods (e.g., unpaired adversarial learning and cycle-consistency constraints) in the SID task is insufficient to learn the underlying relationship from rainy inputs to clean outputs as there exists significant domain gap between the rainy and clean images. In this paper, we develop an effective unpaired SID adversarial framework which explores mutual properties of the unpaired exemplars by a dual contrastive learning manner in a deep feature space, named as DCD-GAN. The proposed method mainly consists of two cooperative branches: Bidirectional Translation Branch (BTB) and Contrastive Guidance Branch (CGB). Specifically, BTB exploits full advantage of the circulatory architecture of adversarial consistency to generate abundant exemplar pairs and excavates latent feature distributions between two domains by equipping it with bidirectional mapping. Simultaneously, CGB implicitly constrains the embeddings of different exemplars in the deep feature space by encouraging the similar feature distributions closer while pushing the dissimilar further away, in order to better facilitate rain removal and help image restoration. Extensive experiments demonstrate that our method performs favorably against existing unpaired deraining approaches on both synthetic and real-world datasets, and generates comparable results against several fully-supervised or semi-supervised models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Unpaired_Deep_Image_Deraining_Using_Dual_Contrastive_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2109.02973 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Unpaired_Deep_Image_Deraining_Using_Dual_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Unpaired_Deep_Image_Deraining_Using_Dual_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Multiple Dense Prediction Tasks From Partially Annotated Data | Wei-Hong Li, Xialei Liu, Hakan Bilen | Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data (i.e. not all the task labels are available for each image), which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Learning_Multiple_Dense_Prediction_Tasks_From_Partially_Annotated_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Learning_Multiple_Dense_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14893 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_Multiple_Dense_Prediction_Tasks_From_Partially_Annotated_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_Multiple_Dense_Prediction_Tasks_From_Partially_Annotated_Data_CVPR_2022_paper.html | CVPR 2022 | null |
Pushing the Performance Limit of Scene Text Recognizer Without Human Annotation | Caiyuan Zheng, Hui Li, Seon-Min Rhee, Seungju Han, Jae-Joon Han, Peng Wang | Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a fully supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-to-synthetic domain gap that restricts model performance. In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly. A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images. A character-level consistency regularization is designed to mitigate the misalignment between characters in sequence recognition. Extensive experiments on standard text recognition benchmarks demonstrate the effectiveness of the proposed method. It can steadily improve existing STR models, and boost an STR model to achieve new state-of-the-art results. To our best knowledge, this is the first consistency regularization based framework that applies successfully to STR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Pushing_the_Performance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07714 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Pushing_the_Performance_Limit_of_Scene_Text_Recognizer_Without_Human_CVPR_2022_paper.html | CVPR 2022 | null |
Boosting 3D Object Detection by Simulating Multimodality on Point Clouds | Wu Zheng, Mingxuan Hong, Li Jiang, Chi-Wing Fu | This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. We design a novel framework to realize the approach: response distillation to focus on the crucial response samples and avoid the background samples; sparse-voxel distillation to learn voxel semantics and relations from the estimated crucial voxels; a fine-grained voxel-to-point distillation to better attend to features of small and distant objects; and instance distillation to further enhance the deep-feature consistency. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors and even surpasses the baseline LiDAR-image detector on the key NDS metric, filling 72% mAP gap between the single- and multi-modality detectors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Boosting_3D_Object_Detection_by_Simulating_Multimodality_on_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Boosting_3D_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Boosting_3D_Object_Detection_by_Simulating_Multimodality_on_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Boosting_3D_Object_Detection_by_Simulating_Multimodality_on_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Low-Cost and Efficient Malaria Detection | Waqas Sultani, Wajahat Nawaz, Syed Javed, Muhammad Sohail Danish, Asma Saadia, Mohsen Ali | Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon the availability of costly microscopes and trained experts to analyze blood-smear slides. Deep learning-based methods have the potential to not only decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes. However, this is hampered by the absence of a reasonable size dataset. One of the most challenging aspects is the reluctance of the experts to annotate the dataset at low magnification on low-cost microscopes. We present a dataset to further the research on malaria microscopy over the low-cost microscopes at low magnification. Our large-scale dataset consists of images of blood-smear slides from several malaria-infected patients, collected through microscopes at two different cost spectrums and multiple magnifications. Malarial cells are annotated for the localization and life-stage classification task on the images collected through the high-cost microscope at high magnification. We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications. Multiple object detectors and domain adaptation methods are presented as the baselines. Furthermore, a partially supervised domain adaptation method is introduced to adapt the object-detector to work on the images collected from the low-cost microscope. The dataset and benchmark models will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sultani_Towards_Low-Cost_and_Efficient_Malaria_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.13656 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sultani_Towards_Low-Cost_and_Efficient_Malaria_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sultani_Towards_Low-Cost_and_Efficient_Malaria_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Neural Light Fields With Ray-Space Embedding | Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, Changil Kim | Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results, but are slow to render, requiring hundreds of network evaluations per pixel to approximate a volume rendering integral. Baking NeRFs into explicit data structures enables efficient rendering, but results in large memory footprints and, in some cases, quality reduction. Additionally, volumetric representations for view synthesis often struggle to represent challenging view dependent effects such as distorted reflections and refractions. We present a novel neural light field representation that, in contrast to prior work, is fast, memory efficient, and excels at modeling complicated view dependence. Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines. At the core of our approach is a ray-space embedding network that maps 4D ray-space into an intermediate, interpolable latent space. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset. In addition, for forward-facing scenes with sparser inputs we achieve results that are competitive with NeRF-based approaches while providing a better speed/quality/memory trade-off with far fewer network evaluations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Attal_Learning_Neural_Light_Fields_With_Ray-Space_Embedding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Attal_Learning_Neural_Light_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Attal_Learning_Neural_Light_Fields_With_Ray-Space_Embedding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Attal_Learning_Neural_Light_Fields_With_Ray-Space_Embedding_CVPR_2022_paper.html | CVPR 2022 | null |
Exposure Normalization and Compensation for Multiple-Exposure Correction | Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, Zhiwei Xiong | Images captured with improper exposures usually bring unsatisfactory visual effects. Previous works mainly focus on either underexposure or overexposure correction, resulting in poor generalization to various exposures. An alternative solution is to mix the multiple exposure data for training a single network. However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance. The key point to address this issue lies in bridging different exposure representations. To achieve this goal, we design a multiple exposure correction framework based on an Exposure Normalization and Compensation (ENC) module. Specifically, the ENC module consists of an exposure normalization part for mapping different exposure features to the exposure-invariant feature space, and a compensation part for integrating the initial features unprocessed by exposure normalization part to ensure the completeness of information. Besides, to further alleviate the imbalanced performance caused by variations in the optimization process, we introduce a parameter regularization fine-tuning strategy to improve the performance of the worst-performed exposure without degrading other exposures. Our model empowered by ENC outperforms the existing methods by more than 2dB and is robust to multiple image enhancement tasks, demonstrating its effectiveness and generalization capability for real-world applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Exposure_Normalization_and_Compensation_for_Multiple-Exposure_Correction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Exposure_Normalization_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Exposure_Normalization_and_Compensation_for_Multiple-Exposure_Correction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Exposure_Normalization_and_Compensation_for_Multiple-Exposure_Correction_CVPR_2022_paper.html | CVPR 2022 | null |
UDA-COPE: Unsupervised Domain Adaptation for Category-Level Object Pose Estimation | Taeyeop Lee, Byeong-Uk Lee, Inkyu Shin, Jaesung Choe, Ukcheol Shin, In So Kweon, Kuk-Jin Yoon | Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE. Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels. We also introduce a bidirectional filtering method between the predicted normalized object coordinate space (NOCS) map and observed point cloud, to not only make our teacher network more robust to the target domain but also to provide more reliable pseudo labels for the student network training. Extensive experimental results demonstrate the effectiveness of our proposed method both quantitatively and qualitatively. Notably, without leveraging target-domain GT labels, our proposed method achieved comparable or sometimes superior performance to existing methods that depend on the GT labels. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_UDA-COPE_Unsupervised_Domain_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_UDA-COPE_Unsupervised_Domain_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_UDA-COPE_Unsupervised_Domain_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Non-Target Knowledge for Few-Shot Semantic Segmentation | Yuanwei Liu, Nian Liu, Qinglong Cao, Xiwen Yao, Junwei Han, Ling Shao | Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL- 5^ i and COCO- 20^ i datasets show that our approach is effective despite its simplicity. Code is available at https://github.com/LIUYUANWEI98/NERTNet | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_Non-Target_Knowledge_for_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.04903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Non-Target_Knowledge_for_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Non-Target_Knowledge_for_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection With Transformers | Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu, Chiew-Lan Tai | LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment, is under-explored. Existing fusion methods are easily affected by such conditions, mainly due to a hard association of LiDAR points and image pixels, established by calibration matrices. We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. Specifically, our TransFusion consists of convolutional backbones and a detection head based on a transformer decoder. The first layer of the decoder predicts initial bounding boxes from a LiDAR point cloud using a sparse set of object queries, and its second decoder layer adaptively fuses the object queries with useful image features, leveraging both spatial and contextual relationships. The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy. We additionally design an image-guided query initialization strategy to deal with objects that are difficult to detect in point clouds. TransFusion achieves state-of-the-art performance on large-scale datasets. We provide extensive experiments to demonstrate its robustness against degenerated image quality and calibration errors. We also extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking, showing its effectiveness and generalization capability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_TransFusion_Robust_LiDAR-Camera_Fusion_for_3D_Object_Detection_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bai_TransFusion_Robust_LiDAR-Camera_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_TransFusion_Robust_LiDAR-Camera_Fusion_for_3D_Object_Detection_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_TransFusion_Robust_LiDAR-Camera_Fusion_for_3D_Object_Detection_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Real-Time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders | Maksim Makarenko, Arturo Burguete-Lopez, Qizhou Wang, Fedor Getman, Silvio Giancola, Bernard Ghanem, Andrea Fratalocchi | Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision. State-of-the-art implementations of snapshot hyperspectral imaging rely on bulky, non-integrated, and expensive optical elements, including lenses, spectrometers, and filters. These macroscopic components do not allow fast data processing for, e.g., real-time and high-resolution videos. This work introduces Hyplex, a new integrated architecture addressing the limitations discussed above. Hyplex is a CMOS-compatible, fast hyperspectral camera that replaces bulk optics with nanoscale metasurfaces inversely designed through artificial intelligence. Hyplex does not require spectrometers but makes use of conventional monochrome cameras, opening up real-time and high-resolution hyperspectral imaging at inexpensive costs. Hyplex exploits a model-driven optimization, which connects the physical metasurfaces layer with modern visual computing approaches based on end-to-end training. We design and implement a prototype version of Hyplex and compare its performance against the state-of-the-art for typical imaging tasks such as spectral reconstruction and semantic segmentation. In all benchmarks, Hyplex reports the smallest reconstruction error. In addition, to the best of the authors' knowledge, we created FVgNET, the largest publicly available labeled hyperspectral dataset for semantic segmentation tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Makarenko_Real-Time_Hyperspectral_Imaging_in_Hardware_via_Trained_Metasurface_Encoders_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Makarenko_Real-Time_Hyperspectral_Imaging_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Makarenko_Real-Time_Hyperspectral_Imaging_in_Hardware_via_Trained_Metasurface_Encoders_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Makarenko_Real-Time_Hyperspectral_Imaging_in_Hardware_via_Trained_Metasurface_Encoders_CVPR_2022_paper.html | CVPR 2022 | null |
Clean Implicit 3D Structure From Noisy 2D STEM Images | Hannah Kniesel, Timo Ropinski, Tim Bergner, Kavitha Shaga Devan, Clarissa Read, Paul Walther, Tobias Ritschel, Pedro Hermosilla | Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components. Unfortunately, these 2D images can be too noisy to be fused into a useful 3D structure and facilitating good denoisers is challenging due to the lack of clean-noisy pairs. Additionally, representing detailed 3D structure can be difficult even for clean data when using regular 3D grids. Addressing these two limitations, we suggest a differentiable image formation model for STEM, allowing to learn a joint model of 2D sensor noise in STEM together with an implicit 3D model. We show, that the combination of these models are able to successfully disentangle 3D signal and noise without supervision and outperform at the same time several baselines on synthetic and real data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kniesel_Clean_Implicit_3D_Structure_From_Noisy_2D_STEM_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kniesel_Clean_Implicit_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kniesel_Clean_Implicit_3D_Structure_From_Noisy_2D_STEM_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kniesel_Clean_Implicit_3D_Structure_From_Noisy_2D_STEM_Images_CVPR_2022_paper.html | CVPR 2022 | null |
UKPGAN: A General Self-Supervised Keypoint Detector | Yang You, Wenhai Liu, Yanjie Ze, Yong-Lu Li, Weiming Wang, Cewu Lu | Keypoint detection is an essential component for the object registration and alignment. In this work, we reckon keypoint detection as information compression, and force the model to distill out important points of an object. Based on this, we propose UKPGAN, a general self-supervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints align well with human annotated keypoint labels, and can be applied to SMPL human bodies under various non-rigid deformations. Furthermore, our keypoint detector trained on clean object collections generalizes well to real-world scenarios, thus further improves geometric registration when combined with off-the-shelf point descriptors. Repeatability experiments show that our model is stable under both rigid and non-rigid transformations, with local reference frame estimation. Our code is available on https://github.com/qq456cvb/UKPGAN. | https://openaccess.thecvf.com/content/CVPR2022/papers/You_UKPGAN_A_General_Self-Supervised_Keypoint_Detector_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/You_UKPGAN_A_General_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.11974 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/You_UKPGAN_A_General_Self-Supervised_Keypoint_Detector_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/You_UKPGAN_A_General_Self-Supervised_Keypoint_Detector_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Optimal K-Space Acquisition and Reconstruction Using Physics-Informed Neural Networks | Wei Peng, Li Feng, Guoying Zhao, Fang Liu | The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred the development of various acceleration methods, typically through heuristically undersampling of the associated measurement domain known as k-space. Recently, deep neural networks have been applied to reconstruct undersampled k-space and shown improved reconstruction performance. While most methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e.g., random Cartesian undersampling or standard non-Cartesian sampling, to date, there is limited research that aims to learn and optimize k-space sampling strategies using deep neural networks. In this work, we propose a novel framework to learn optimized k-space sampling trajectories using deep learning by considering it as an Ordinary Differential Equation (ODE) problem that can be solved using neural ODE. In particular, the sampling of k-space data is framed as a dynamic system, in which the control points serve as an initial state and a physical-conditioned neural ODE is formulated to approximate the system. Moreover, we also enforce additional constraints on gradient slew rate and amplitude in trajectory learning, so that severe gradient-indued artifacts can be minimized. Furthermore, we have also demonstrated that sampling trajectory optimization and MRI reconstruction can be jointly trained, such that the optimized trajectory is task-oriented and can enhance overall image reconstruction performance. Experiments were conducted on different in-vivo dataset (e.g., Brain and Knee) with different contrast. Initial results have shown that our proposed method is able to generate better image quality in accelerated MRI compared to conventional undersampling schemes in both Cartesian and non-Cartesian acquisitions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_Learning_Optimal_K-Space_Acquisition_and_Reconstruction_Using_Physics-Informed_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_Learning_Optimal_K-Space_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Learning_Optimal_K-Space_Acquisition_and_Reconstruction_Using_Physics-Informed_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Learning_Optimal_K-Space_Acquisition_and_Reconstruction_Using_Physics-Informed_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Leveraging Adversarial Examples To Quantify Membership Information Leakage | Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida | The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today. Identifying training data based on a trained model is a standard way of measuring the privacy risks induced by the model. We develop a novel approach to address the problem of membership inference in pattern recognition models, relying on information provided by adversarial examples. The strategy we propose consists of measuring the magnitude of a perturbation necessary to build an adversarial example. Indeed, we argue that this quantity reflects the likelihood of belonging to the training data. Extensive numerical experiments on multivariate data and an array of state-of-the-art target models show that our method performs comparable or even outperforms state-of-the-art strategies, but without requiring any additional training samples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Del_Grosso_Leveraging_Adversarial_Examples_To_Quantify_Membership_Information_Leakage_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Del_Grosso_Leveraging_Adversarial_Examples_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Del_Grosso_Leveraging_Adversarial_Examples_To_Quantify_Membership_Information_Leakage_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Del_Grosso_Leveraging_Adversarial_Examples_To_Quantify_Membership_Information_Leakage_CVPR_2022_paper.html | CVPR 2022 | null |
Raw High-Definition Radar for Multi-Task Learning | Julien Rebut, Arthur Ouaknine, Waqas Malik, Patrick Pérez | With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades. Recent progress toward High Definition (HD) Imaging radar has driven the angular resolution below the degree, thus approaching laser scanning performance. However, the amount of data a HD radar delivers and the computational cost to estimate the angular positions remain a challenge. In this paper, we propose a novel HD radar sensing model, FFT-RadNet, that eliminates the overhead of computing the range-azimuth-Doppler 3D tensor, learning instead to recover angles from a range-Doppler spectrum. FFTRadNet is trained both to detect vehicles and to segment free driving space. On both tasks, it competes with the most recent radar-based models while requiring less compute and memory. Also, we collected and annotated 2-hour worth of raw data from synchronized automotive-grade sensors (camera, laser, HD radar) in various environments (city street, highway, countryside road). This unique dataset, nick-named RADIal for "Radar, LiDAR et al.", is available at https://github.com/valeoai/RADIal. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rebut_Raw_High-Definition_Radar_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.10646 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rebut_Raw_High-Definition_Radar_for_Multi-Task_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Point-NeRF: Point-Based Neural Radiance Fields | Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, Ulrich Neumann | Volumetric neural rendering methods like NeRF generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be fine-tuned to surpass the visual quality of NeRF with 30X faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Point-NeRF_Point-Based_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Point-NeRF_Point-Based_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Point-NeRF_Point-Based_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Point-NeRF_Point-Based_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Contextual Debiasing for Visual Recognition With Causal Mechanisms | Ruyang Liu, Hao Liu, Ge Li, Haodi Hou, TingHao Yu, Tao Yang | As a common problem in the visual world, contextual bias means the recognition may depend on the co-occurrence context rather than the objects themselves, which is even more severe in multi-label tasks due to multiple targets and the absence of location. Although some studies have focused on tackling the problem, removing the negative effect of context is still challenging because it is difficult to obtain the representation of contextual bias. In this paper, we propose a simple but effective framework employing causal inference to mitigate contextual bias. We first present a Structural Causal Model (SCM) clarifying the causal relation among object representations, context, and predictions. Then, we develop a novel Causal Context Debiasing (CCD) Module to pursue the direct effect of an instance. Specifically, we adopt causal intervention to eliminate the effect of confounder and counterfactual reasoning to obtain a Total Direct Effect (TDE) free from the contextual bias. Note that our CCD framework is orthogonal to existing statistical models and thus can be migrated to any other backbones. Extensive experiments on several multi-label classification datasets demonstrate the superiority of our model over other state-of-the-art baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Contextual_Debiasing_for_Visual_Recognition_With_Causal_Mechanisms_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Contextual_Debiasing_for_Visual_Recognition_With_Causal_Mechanisms_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Contextual_Debiasing_for_Visual_Recognition_With_Causal_Mechanisms_CVPR_2022_paper.html | CVPR 2022 | null |
Complex Video Action Reasoning via Learnable Markov Logic Network | Yang Jin, Linchao Zhu, Yadong Mu | Profiting from the advance of deep convolutional networks, current state-of-the-art video action recognition models have achieved remarkable progress. Nevertheless, most of existing models suffer from low interpretability of the predicted actions. Inspired by the observation that temporally-configured human-object interactions often serve as a key indicator of many actions, this work crafts an action reasoning framework that performs Markov Logic Network (MLN) based probabilistic logical inference. Crucially, we propose to encode an action by first-order logical rules that correspond to the temporal changes of visual relationships in videos. The main contributions of this work are two-fold: 1) Different from existing black-box models, the proposed model simultaneously implements the localization of temporal boundaries and the recognition of action categories by grounding the logical rules of MLN in videos. The weight associated with each such rule further provides an estimate of confidence. These collectively make our model more explainable and robust. 2) Instead of using hand-crafted logical rules in conventional MLN, we develop a data-driven instantiation of the MLN. In specific, a hybrid learning scheme is proposed. It combines MLN's weight learning and reinforcement learning, using the former's results as a self-critic for guiding the latter's training. Additionally, by treating actions as logical predicates, the proposed framework can also be integrated with deep models for further performance boost. Comprehensive experiments on two complex video action datasets (Charades & CAD-120) clearly demonstrate the effectiveness and explainability of our proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Complex_Video_Action_Reasoning_via_Learnable_Markov_Logic_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Complex_Video_Action_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Complex_Video_Action_Reasoning_via_Learnable_Markov_Logic_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Complex_Video_Action_Reasoning_via_Learnable_Markov_Logic_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Per-Clip Video Object Segmentation | Kwanyong Park, Sanghyun Woo, Seoung Wug Oh, In So Kweon, Joon-Young Lee | Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently updated memory of the previous mask. Different from this per-frame inference, we investigate an alternative perspective by treating video object segmentation as clip-wise mask propagation. In this per-clip inference scheme, we update the memory with an interval and simultaneously process a set of consecutive frames (i.e. clip) between the memory updates. The scheme provides two potential benefits: accuracy gain by clip-level optimization and efficiency gain by parallel computation of multiple frames. To this end, we propose a new method tailored for the per-clip inference. Specifically, we first introduce a clip-wise operation to refine the features based on intra-clip correlation. In addition, we employ a progressive matching mechanism for efficient information-passing within a clip. With the synergy of two modules and a newly proposed per-clip based training, our network achieves state-of-the-art performance on Youtube-VOS 2018/2019 val (84.6% and 84.6%) and DAVIS 2016/2017 val (91.9% and 86.1%). Furthermore, our model shows a great speed-accuracy trade-off with varying memory update intervals, which leads to huge flexibility. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Per-Clip_Video_Object_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Per-Clip_Video_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Per-Clip_Video_Object_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Per-Clip_Video_Object_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Set Similarity for Dense Self-Supervised Representation Learning | Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu | By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks. However, the pixel-level correspondence tends to be noisy because of many similar misleading pixels, e.g., backgrounds. To address this issue, in this paper, we propose to explore set similarity (SetSim) for dense self-supervised representation learning. We generalize pixel-wise similarity learning to set-wise one to improve the robustness because sets contain more semantic and structure information. Specifically, by resorting to attentional features of views, we establish the corresponding set, thus filtering out noisy backgrounds that may cause incorrect correspondences. Meanwhile, these attentional features can keep the coherence of the same image across different views to alleviate semantic inconsistency. We further search the cross-view nearest neighbours of sets and employ the structured neighbourhood information to enhance the robustness. Empirical evaluations demonstrate that SetSim surpasses or is on par with state-of-the-art methods on object detection, keypoint detection, instance segmentation, and semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Exploring_Set_Similarity_for_Dense_Self-Supervised_Representation_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2107.08712 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Exploring_Set_Similarity_for_Dense_Self-Supervised_Representation_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Exploring_Set_Similarity_for_Dense_Self-Supervised_Representation_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Coarse-To-Fine Feature Mining for Video Semantic Segmentation | Guolei Sun, Yun Liu, Henghui Ding, Thomas Probst, Luc Van Gool | The contextual information plays a core role in semantic segmentation. As for video semantic segmentation, the contexts include static contexts and motional contexts, corresponding to static content and moving content in a video clip, respectively. The static contexts are well exploited in image semantic segmentation by learning multi-scale and global/long-range features. The motional contexts are studied in previous video semantic segmentation. However, there is no research about how to simultaneously learn static and motional contexts which are highly correlated and complementary to each other. To address this problem, we propose a Coarse-to-Fine Feature Mining (CFFM) technique to learn a unified presentation of static contexts and motional contexts. This technique consists of two parts: coarse-to-fine feature assembling and cross-frame feature mining. The former operation prepares data for further processing, enabling the subsequent joint learning of static and motional contexts. The latter operation mines useful information/contexts from the sequential frames to enhance the video contexts of the features of the target frame. The enhanced features can be directly applied for the final prediction. Experimental results on popular benchmarks demonstrate that the proposed CFFM performs favorably against state-of-the-art methods for video semantic segmentation. Our implementation is available at https://github.com/GuoleiSun/VSS-CFFM | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Coarse-To-Fine_Feature_Mining_for_Video_Semantic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.03330 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Coarse-To-Fine_Feature_Mining_for_Video_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Coarse-To-Fine_Feature_Mining_for_Video_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
ONCE-3DLanes: Building Monocular 3D Lane Detection | Fan Yan, Ming Nie, Xinyue Cai, Jianhua Han, Hang Xu, Zhen Yang, Chaoqiang Ye, Yanwei Fu, Michael Bi Mi, Li Zhang | We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus necessary and enables effective and safe driving. However, existing 3D lane detection datasets are either unpublished or synthesized from a simulated environment, severely hampering the development of this field. In this paper, we take steps towards addressing these issues. By exploiting the explicit relationship between point clouds and image pixels, a dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations in 211K road scenes. In addition, we present an extrinsic-free, anchor-free method, called SALAD, regressing the 3D coordinates of lanes in image view without converting the feature map into the bird's-eye view (BEV). To facilitate future research on 3D lane detection, we benchmark the dataset and provide a novel evaluation metric, performing extensive experiments of both existing approaches and our proposed method. The aim of our work is to revive the interest of 3D lane detection in a real-world scenario. We believe our work can lead to expected and unexpected innovations in both academia and industry. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_ONCE-3DLanes_Building_Monocular_3D_Lane_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_ONCE-3DLanes_Building_Monocular_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_ONCE-3DLanes_Building_Monocular_3D_Lane_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_ONCE-3DLanes_Building_Monocular_3D_Lane_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly but Deeply Supervised Occlusion-Reasoned Parametric Road Layouts | Buyu Liu, Bingbing Zhuang, Manmohan Chandraker | We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird's-eye-view (BEV) space. In contrast to prior works that require dense supervision such as semantic labels in perspective view, our method only requires human annotations for parametric attributes that are cheaper and less ambiguous to obtain. To solve this challenging task, our design is comprised of modules that incorporate inductive biases to learn occlusion-reasoning, geometric transformation and semantic abstraction, where each module may be supervised by appropriately transforming the parametric annotations. We demonstrate how our design choices and proposed deep supervision help achieve meaningful representations and accurate predictions. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state-of-the-art results with considerably less human supervision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Weakly_but_Deeply_Supervised_Occlusion-Reasoned_Parametric_Road_Layouts_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Weakly_but_Deeply_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.06730 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Weakly_but_Deeply_Supervised_Occlusion-Reasoned_Parametric_Road_Layouts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Weakly_but_Deeply_Supervised_Occlusion-Reasoned_Parametric_Road_Layouts_CVPR_2022_paper.html | CVPR 2022 | null |
Compressing Models With Few Samples: Mimicking Then Replacing | Huanyu Wang, Junjie Liu, Xin Ma, Yang Yong, Zhenhua Chai, Jianxin Wu | Few-sample compression aims to compress a big redundant model into a small compact one with only few samples. If we fine-tune models with these limited few samples directly, models will be vulnerable to overfit and learn almost nothing. Hence, previous methods optimize the compressed model layer-by-layer and try to make every layer have the same outputs as the corresponding layer in the teacher model, which is cumbersome. In this paper, we propose a new framework named Mimicking then Replacing (MiR) for few-sample compression, which firstly urges the pruned model to output the same features as the teacher's in the penultimate layer, and then replaces teacher's layers before penultimate with a well-tuned compact one. Unlike previous layer-wise reconstruction methods, our MiR optimizes the entire network holistically, which is not only simple and effective, but also unsupervised and general. MiR outperforms previous methods with large margins. Codes is available at https://github.com/cjnjuwhy/MiR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Compressing_Models_With_Few_Samples_Mimicking_Then_Replacing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Compressing_Models_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02620 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Compressing_Models_With_Few_Samples_Mimicking_Then_Replacing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Compressing_Models_With_Few_Samples_Mimicking_Then_Replacing_CVPR_2022_paper.html | CVPR 2022 | null |
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning | Minxue Tang, Xuefei Ning, Yitu Wang, Jingwei Sun, Yu Wang, Hai Li, Yiran Chen | Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor---an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by 34% 99% and 26% 51% on FMNIST and CIFAR-10, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_FedCor_Correlation-Based_Active_Client_Selection_Strategy_for_Heterogeneous_Federated_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_FedCor_Correlation-Based_Active_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2103.13822 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_FedCor_Correlation-Based_Active_Client_Selection_Strategy_for_Heterogeneous_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_FedCor_Correlation-Based_Active_Client_Selection_Strategy_for_Heterogeneous_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Modulated Contrast for Versatile Image Synthesis | Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Rongliang Wu, Shijian Lu | Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks. Predominant approaches measure the inter-image distance by computing pointwise absolute deviations, which tends to estimate the median of instance distributions and leads to blurs and artifacts in the generated images. This paper presents MoNCE, a versatile metric that introduces image contrast to learn a calibrated metric for the perception of multifaceted inter-image distances. Unlike vanilla contrast which indiscriminately pushes negative samples from the anchor regardless of their similarity, we propose to re-weight the pushing force of negative samples adaptively according to their similarity to the anchor, which facilitates the contrastive learning from informative negative samples. Since multiple patch-level contrastive objectives are involved in image distance measurement, we introduce optimal transport in MoNCE to modulate the pushing force of negative samples collaboratively across multiple contrastive objectives. Extensive experiments over multiple image translation tasks show that the proposed MoNCE outperforms various prevailing metrics substantially. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhan_Modulated_Contrast_for_Versatile_Image_Synthesis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.09333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhan_Modulated_Contrast_for_Versatile_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhan_Modulated_Contrast_for_Versatile_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
PokeBNN: A Binary Pursuit of Lightweight Accuracy | Yichi Zhang, Zhiru Zhang, Lukasz Lew | Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the compute intensity but existing models suffer from low quality. To overcome this deficiency, we propose PokeConv, a binary convolution block which improves quality of BNNs by techniques such as adding multiple residual paths, and tuning the activation function. We apply it to ResNet-50 and optimize ResNet's initial convolutional layer which is hard to binarize. We name the resulting network family PokeBNN. These techniques are chosen to yield favorable improvements in both top-1 accuracy and the network's cost. In order to enable joint optimization of the cost together with accuracy, we define arithmetic computation effort (ACE), a hardware- and energy-inspired cost metric for quantized and binarized networks. We also identify a need to optimize an under-explored hyper-parameter controlling the binarization gradient approximation. We establish a new, strong state-of-the-art (SOTA) on top-1 accuracy together with commonly-used CPU64 cost, ACE cost and network size metrics. ReActNet-Adam, the previous SOTA in BNNs, achieved a 70.5% top-1 accuracy with 7.9 ACE. A small variant of PokeBNN achieves 70.5% top-1 with 2.6 ACE, more than 3x reduction in cost; a larger PokeBNN achieves 75.6% top-1 with 7.8 ACE, more than 5% improvement in accuracy without increasing the cost. PokeBNN implementation in JAX / Flax and reproduction instructions are open sourced. Source code and reproduction instructions are available in AQT repository: https://github.com/google/aqt. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_PokeBNN_A_Binary_Pursuit_of_Lightweight_Accuracy_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_PokeBNN_A_Binary_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00133 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_PokeBNN_A_Binary_Pursuit_of_Lightweight_Accuracy_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_PokeBNN_A_Binary_Pursuit_of_Lightweight_Accuracy_CVPR_2022_paper.html | CVPR 2022 | null |
HumanNeRF: Efficiently Generated Human Radiance Field From Sparse Inputs | Fuqiang Zhao, Wei Yang, Jiakai Zhang, Pei Lin, Yingliang Zhang, Jingyi Yu, Lan Xu | Recent neural human representations can produce high-quality multi-view rendering but require using dense multi-view inputs and costly training. They are hence largely limited to static models as training each frame is infeasible. We present HumanNeRF - a neural representation with efficient generalization ability - for high-fidelity free-view synthesis of dynamic humans. Analogous to how IBRNet assists NeRF by avoiding per-scene training, HumanNeRF employs an aggregated pixel-alignment feature across multi-view inputs along with a pose embedded non-rigid deformation field for tackling dynamic motions. The raw HumanNeRF can already produce reasonable rendering on sparse video inputs of unseen subjects and camera settings. To further improve the rendering quality, we augment our solution with in-hour scene-specific fine-tuning, and an appearance blending module for combining the benefits of both neural volumetric rendering and neural texture blending. Extensive experiments on various multi-view dynamic human datasets demonstrate effectiveness of our approach in synthesizing photo-realistic free-view humans under challenging motions and with very sparse camera view inputs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_HumanNeRF_Efficiently_Generated_Human_Radiance_Field_From_Sparse_Inputs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_HumanNeRF_Efficiently_Generated_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02789 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_HumanNeRF_Efficiently_Generated_Human_Radiance_Field_From_Sparse_Inputs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_HumanNeRF_Efficiently_Generated_Human_Radiance_Field_From_Sparse_Inputs_CVPR_2022_paper.html | CVPR 2022 | null |
Zoom in and Out: A Mixed-Scale Triplet Network for Camouflaged Object Detection | Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, Huchuan Lu | The recently proposed camouflaged object detection (COD) attempts to segment objects that are visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from high intrinsic similarity between the camouflaged objects and their background, the objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To deal with these problems, we propose a mixed-scale triplet network, ZoomNet, which mimics the behavior of humans when observing vague images, i.e., zooming in and out. Specifically, our ZoomNet employs the zoom strategy to learn the discriminative mixed-scale semantics by the designed scale integration unit and hierarchical mixed-scale unit, which fully explores imperceptible clues between the candidate objects and background surroundings. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization constraint, uncertainty-aware loss, to promote the model to accurately produce predictions with higher confidence in candidate regions. Without bells and whistles, our proposed highly task-friendly model consistently surpasses the existing 23 state-of-the-art methods on four public datasets. Besides, the superior performance over the recent cutting-edge models on the SOD task also verifies the effectiveness and generality of our model. The code will be available at https://github.com/lartpang/ZoomNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pang_Zoom_in_and_Out_A_Mixed-Scale_Triplet_Network_for_Camouflaged_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pang_Zoom_in_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02688 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Zoom_in_and_Out_A_Mixed-Scale_Triplet_Network_for_Camouflaged_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Zoom_in_and_Out_A_Mixed-Scale_Triplet_Network_for_Camouflaged_CVPR_2022_paper.html | CVPR 2022 | null |
Identifying Ambiguous Similarity Conditions via Semantic Matching | Han-Jia Ye, Yi Shi, De-Chuan Zhan | Rich semantics inside an image result in its ambiguous relationship with others, i.e., two images could be similar in one condition but dissimilar in another. Given triplets like "aircraft" is similar to "bird" than "train", Weakly Supervised Conditional Similarity Learning (WS-CSL) learns multiple embeddings to match semantic conditions without explicit condition labels such as "can fly". However, similarity relationships in a triplet are uncertain except providing a condition. For example, the previous comparison becomes invalid once the conditional label changes to "is vehicle". To this end, we introduce a novel evaluation criterion by predicting the comparison's correctness after assigning the learned embeddings to their optimal conditions, which measures how much WS-CSL could cover latent semantics as the supervised model. Furthermore, we propose the Distance Induced Semantic COndition VERification Network (DiscoverNET), which characterizes the instance-instance and triplets-condition relations in a "decompose-and-fuse" manner. To make the learned embeddings cover all semantics, DiscoverNET utilizes a set module or an additional regularizer over the correspondence between a triplet and a condition. DiscoverNET achieves state-of-the-art performance on benchmarks like UT-Zappos-50k and Celeb-A w.r.t. different criteria. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Identifying_Ambiguous_Similarity_Conditions_via_Semantic_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Identifying_Ambiguous_Similarity_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04053 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Identifying_Ambiguous_Similarity_Conditions_via_Semantic_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Identifying_Ambiguous_Similarity_Conditions_via_Semantic_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
MISF: Multi-Level Interactive Siamese Filtering for High-Fidelity Image Inpainting | Xiaoguang Li, Qing Guo, Di Lin, Ping Li, Wei Feng, Song Wang | Although achieving significant progress, existing deep generative inpainting methods still show low generalization across different scenes. As a result, the generated images usually contain artifacts or the filled pixels differ greatly from the ground truth, making them far from real-world applications. Image-level predictive filtering is a widely used restoration technique by predicting suitable kernels adaptively according to different input scenes. Inspired by this inherent advantage, we explore the possibility of addressing image inpainting as a filtering task. To this end, we first study the advantages and challenges of the image-level predictive filtering for inpainting: the method can preserve local structures and avoid artifacts but fails to fill large missing areas. Then, we propose the semantic filtering by conducting filtering on deep feature level, which fills the missing semantic information but fails to recover the details. To address the issues while adopting the respective advantages, we propose a novel filtering technique, i.e., Multi-level Interactive Siamese Filtering (MISF) containing two branches: kernel prediction branch (KPB) and semantic & image filtering branch (SIFB). These two branches are interactively linked: SIFB provides multi-level features for KPB while KPB predicts dynamic kernels for SIFB. As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting. Moreover, we discuss the relationship between MISF and the naive encoder-decoder-based inpainting, inferring that MISF provides novel dynamic convolutional operations to enhance the high generalization capability across scenes. We validate our method on three challenging datasets, i.e., Dunhuang, Places2, and CelebA. Our method outperforms state-of-the-art baselines on four metrics, i.e., L1, PSNR, SSIM, and LPIPS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_MISF_Multi-Level_Interactive_Siamese_Filtering_for_High-Fidelity_Image_Inpainting_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.06304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MISF_Multi-Level_Interactive_Siamese_Filtering_for_High-Fidelity_Image_Inpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MISF_Multi-Level_Interactive_Siamese_Filtering_for_High-Fidelity_Image_Inpainting_CVPR_2022_paper.html | CVPR 2022 | null |
Cascade Transformers for End-to-End Person Search | Rui Yu, Dawei Du, Rodney LaLonde, Daniel Davila, Christopher Funk, Anthony Hoogs, Brian Clipp | The goal of person search is to localize a target person from a gallery set of scene images, which is extremely challenging due to large scale variations, pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. Our three-stage cascade design focuses on detecting people in the first stage, while later stages simultaneously and progressively refine the representation for person detection and re-identification. At each stage the occluded attention transformer applies tighter intersection over union thresholds, forcing the network to learn coarse-to-fine pose/scale invariant features. Meanwhile, we calculate each detection's occluded attention to differentiate a person's tokens from other people or the background. In this way, we simulate the effect of other objects occluding a person of interest at the token-level. Through comprehensive experiments, we demonstrate the benefits of our method by achieving state-of-the-art performance on two benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Cascade_Transformers_for_End-to-End_Person_Search_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.09642 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Cascade_Transformers_for_End-to-End_Person_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Cascade_Transformers_for_End-to-End_Person_Search_CVPR_2022_paper.html | CVPR 2022 | null |
MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection | Bumsoo Kim, Jonghwan Mun, Kyoung-Woon On, Minchul Shin, Junhyun Lee, Eun-Sol Kim | Human-Object Interaction (HOI) detection is the task of identifying a set of <human, object, interaction> triplets from an image. Recent work proposed transformer encoder-decoder architectures that successfully eliminated the need for many hand-designed components in HOI detection through end-to-end training. However, they are limited to single-scale feature resolution, providing suboptimal performance in scenes containing humans, objects, and their interactions with vastly different scales and distances. To tackle this problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered by two novel HOI-aware deformable attention modules called Dual-Entity attention and Entity-conditioned Context attention. While existing deformable attention comes at a huge cost in HOI detection performance, our proposed attention modules of MSTR learn to effectively attend to sampling points that are essential to identify interactions. In experiments, we achieve the new state-of-the-art performance on two HOI detection benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_MSTR_Multi-Scale_Transformer_for_End-to-End_Human-Object_Interaction_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_MSTR_Multi-Scale_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14709 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_MSTR_Multi-Scale_Transformer_for_End-to-End_Human-Object_Interaction_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_MSTR_Multi-Scale_Transformer_for_End-to-End_Human-Object_Interaction_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
LSVC: A Learning-Based Stereo Video Compression Framework | Zhenghao Chen, Guo Lu, Zhihao Hu, Shan Liu, Wei Jiang, Dong Xu | In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i.e., stereo videos from autonomous driving applications) from both left and right views. Specifically, when compressing the current frame from each view, our framework reduces temporal redundancy by performing motion compensation using the reconstructed intra-view adjacent frame and at the same time exploits binocular redundancy by conducting disparity compensation using the latest reconstructed cross-view frame. Moreover, to effectively compress the introduced motion and disparity offsets for better compensation, we further propose two novel schemes called motion residual compression and disparity residual compression to respectively generate the predicted motion offset and disparity offset from the previously compressed motion offset and disparity offset, such that we can more effectively compress residual offset information for better bit-rate saving. Overall, the entire framework is implemented by the fully-differentiable modules and can be optimized in an end-to-end manner. Our comprehensive experiments on three automotive stereo video benchmarks Cityscapes, KITTI 2012 and KITTI 2015 demonstrate that our proposed framework outperforms the learning-based single-view video codec and the traditional hand-crafted multi-view video codec. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.html | CVPR 2022 | null |
How Do You Do It? Fine-Grained Action Understanding With Pseudo-Adverbs | Hazel Doughty, Cees G. M. Snoek | We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a method which recognizes adverbs across different actions. However, such fine-grained annotations are difficult to obtain and their long-tailed nature makes it challenging to recognize adverbs in rare action-adverb compositions. Our approach therefore uses semi-supervised learning with multiple adverb pseudo-labels to leverage videos with only action labels. Combined with adaptive thresholding of these pseudo-adverbs we are able to make efficient use of the available data while tackling the long-tailed distribution. Additionally, we gather adverb annotations for three existing video retrieval datasets, which allows us to introduce the new tasks of recognizing adverbs in unseen action-adverb compositions and unseen domains. Experiments demonstrate the effectiveness of our method,which outperforms prior work in recognizing adverbs and semi-supervised works adapted for adverb recognition. We also show how adverbs can relate fine-grained actions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Doughty_How_Do_You_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.12344 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.html | CVPR 2022 | null |
InsetGAN for Full-Body Image Generation | Anna Frühstück, Krishna Kumar Singh, Eli Shechtman, Niloy J. Mitra, Peter Wonka, Jingwan Lu | While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities, hairstyles, clothing, and the variance in pose. Instead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e.g., human body) and a set of specialized GANs, or insets, focus on different parts (e.g., faces, shoes) that can be seamlessly inserted onto the global canvas. We model the problem as jointly exploring the respective latent spaces such that the generated images can be combined, by inserting the parts from the specialized generators onto the global canvas, without introducing seams. We demonstrate the setup by combining a full body GAN with a dedicated high-quality face GAN to produce plausible-looking humans. We evaluate our results with quantitative metrics and user studies. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fruhstuck_InsetGAN_for_Full-Body_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fruhstuck_InsetGAN_for_Full-Body_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fruhstuck_InsetGAN_for_Full-Body_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fruhstuck_InsetGAN_for_Full-Body_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors | Sivapriya Vellaichamy, Matthew Hull, Zijie J. Wang, Nilaksh Das, ShengYun Peng, Haekyu Park, Duen Horng (Polo) Chau | With deep learning based systems performing exceedingly well in many vision-related tasks, a major concern with their widespread deployment especially in safety-critical applications is their susceptibility to adversarial attacks. We propose DetectorDetective, an interactive visual tool that aims to help users better understand the behaviors of a model as adversarial images journey through an object detector. DetectorDetective enables users to easily learn about how the three key modules of the Faster R-CNN object detector -- Feature Pyramidal Network, Region Proposal Network, and Region Of Interest Head--respond to a user-selected benign image and its adversarial version. Visualizations about the progressive changes in the intermediate features among such modules help users gain insights into the impact of adversarial attacks, and perform side-by-side comparisons between the benign and adversarial responses. Furthermore, DetectorDetective displays saliency maps for the input images to comparatively highlight image regions that contribute to attack success. DetectorDetective complements adversarial machine learning research on object detection by providing a user-friendly interactive tool for inspecting and understanding model responses. DetectorDetective is available at the following public demo link: https://poloclub.github.io/detector-detective. A video demo is available at https://youtu.be/5C3Klh87CZI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Vellaichamy_DetectorDetective_Investigating_the_Effects_of_Adversarial_Examples_on_Object_Detectors_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vellaichamy_DetectorDetective_Investigating_the_Effects_of_Adversarial_Examples_on_Object_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vellaichamy_DetectorDetective_Investigating_the_Effects_of_Adversarial_Examples_on_Object_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
SOMSI: Spherical Novel View Synthesis With Soft Occlusion Multi-Sphere Images | Tewodros Habtegebrial, Christiano Gava, Marcel Rogge, Didier Stricker, Varun Jampani | Spherical novel view synthesis (SNVS) is the task of estimating 360 views at dynamic novel views given a set of 360 input views. Prior arts learn multi-sphere image (MSI) representations that enables fast rendering times but are only limited to modelling low-dimensional color values. Modelling high-dimensional appearance features in MSI can result in better view synthesis, but it is not feasible to represent high-dimensional features in a large number (>64) of MSI spheres. We propose a novel MSI representation called Soft Occlusion MSI (SOMSI) that enables modelling high-dimensional appearance features in MSI while retaining the fast rendering times of a standard MSI. Our key insight is to model appearance features in a smaller set (e.g. 3) of occlusion levels instead of larger number of MSI levels. Experiments on both synthetic and real-world scenes demonstrate that using SOMSI can provide a good balance between accuracy and runtime. SOMSI can produce considerably better results compared to MSI based MODS, while having similar fast rendering time. SOMSI view synthesis quality is on-par with state-of-the-art NeRF like model while being 2 orders of magnitude faster. For code, additional results and data, please visit https://tedyhabtegebrial.github.io/somsi. | https://openaccess.thecvf.com/content/CVPR2022/papers/Habtegebrial_SOMSI_Spherical_Novel_View_Synthesis_With_Soft_Occlusion_Multi-Sphere_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Habtegebrial_SOMSI_Spherical_Novel_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Habtegebrial_SOMSI_Spherical_Novel_View_Synthesis_With_Soft_Occlusion_Multi-Sphere_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Habtegebrial_SOMSI_Spherical_Novel_View_Synthesis_With_Soft_Occlusion_Multi-Sphere_Images_CVPR_2022_paper.html | CVPR 2022 | null |
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching | Yaya Shi, Xu Yang, Haiyang Xu, Chunfeng Yuan, Bing Li, Weiming Hu, Zheng-Jun Zha | Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. However, they have some insuperable drawbacks, e.g., they cannot handle videos without references, and they may result in biased evaluation due to the one-to-many nature of video-to-text and the neglect of visual relevance. From the human evaluator's viewpoint, a high-quality caption should be consistent with the provided video, but not necessarily be similar to the reference in literal or semantics. Inspired by human evaluation, we propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning, which directly measures similarity between video and candidate captions. Benefiting from the recent development of large-scale pre-training models, we exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore. Specifically, EMScore combines matching scores of both coarse-grained (video and caption) and fine-grained (frames and words) levels, which takes the overall understanding and detailed characteristics of the video into account. Furthermore, considering the potential information gain, EMScore can be flexibly extended to the conditions where human-labeled references are available. Last but not least, we collect VATEX-EVAL and ActivityNet-FOIl datasets to systematically evaluate the existing metrics. VATEX-EVAL experiments demonstrate that EMScore has higher human correlation and lower reference dependency. ActivityNet-FOIL experiment verifies that EMScore can effectively identify hallucinating captions. Code and datasets are available at https://github.com/shiyaya/emscore. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_EMScore_Evaluating_Video_Captioning_via_Coarse-Grained_and_Fine-Grained_Embedding_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_EMScore_Evaluating_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.08919 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_EMScore_Evaluating_Video_Captioning_via_Coarse-Grained_and_Fine-Grained_Embedding_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_EMScore_Evaluating_Video_Captioning_via_Coarse-Grained_and_Fine-Grained_Embedding_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
SNR-Aware Low-Light Image Enhancement | Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, Jiaya Jia | This paper presents a new solution for low-light image enhancement by collectively exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to dynamically enhance pixels with spatial-varying operations. They are long-range operations for image regions of extremely low Signal-to-Noise-Ratio (SNR) and short-range operations for other regions. We propose to take an SNR prior to guide the feature fusion and formulate the SNR-aware transformer with a new self-attention model to avoid tokens from noisy image regions of very low SNR. Extensive experiments show that our framework consistently achieves better performance than SOTA approaches on seven representative benchmarks with the same structure. Also, we conducted a large-scale user study with 100 participants to verify the superior perceptual quality of our results. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_SNR-Aware_Low-Light_Image_Enhancement_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SNR-Aware_Low-Light_Image_Enhancement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SNR-Aware_Low-Light_Image_Enhancement_CVPR_2022_paper.html | CVPR 2022 | null |
3D Common Corruptions and Data Augmentation | Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov, Amir Zamir | We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike existing approaches such as Common Corruptions, the geometry of the scene is incorporated in the transformations -- thus leading to corruptions that are more likely to occur in the real world. We also introduce a set of semantic corruptions (e.g. natural object occlusions). We show these transformations are 'efficient' (can be computed on-the-fly), 'extendable' (can be applied on most image datasets), expose vulnerability of existing models, and can effectively make models more robust when employed as '3D data augmentation' mechanisms. The evaluations on several tasks and datasets suggest incorporating 3D information into benchmarking and training opens up a promising direction for robustness research. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kar_3D_Common_Corruptions_and_Data_Augmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01441 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kar_3D_Common_Corruptions_and_Data_Augmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kar_3D_Common_Corruptions_and_Data_Augmentation_CVPR_2022_paper.html | CVPR 2022 | null |
PoseTriplet: Co-Evolving 3D Human Pose Estimation, Imitation, and Hallucination Under Self-Supervision | Kehong Gong, Bingbing Li, Jianfeng Zhang, Tao Wang, Jing Huang, Michael Bi Mi, Jiashi Feng, Xinchao Wang | Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses. In this paper, we propose a novel self-supervised approach that allows us to explicitly generate 2D-3D pose pairs for augmenting supervision, through a self-enhancing dual-loop learning framework. This is made possible via introducing a reinforcement-learning-based imitator, which is learned jointly with a pose estimator alongside a pose hallucinator; the three components form two loops during the training process, complementing and strengthening one another. Specifically, the pose estimator transforms an input 2D pose sequence to a low-fidelity 3D output, which is then enhanced by the imitator that enforces physical constraints. The refined 3D poses are subsequently fed to the hallucinator for producing even more diverse data, which are, in turn, strengthened by the imitator and further utilized to train the pose estimator. Such a co-evolution scheme, in practice, enables training a pose estimator on self-generated motion data without relying on any given 3D data. Extensive experiments across various benchmarks demonstrate that our approach yields encouraging results significantly outperforming the state of the art and, in some cases, even on par with results of fully-supervised methods. Notably, it achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset evaluation setup, improving upon the previous best self-supervised method by 8.6%. Code is available at https://github.com/Garfield-kh/PoseTriplet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gong_PoseTriplet_Co-Evolving_3D_Human_Pose_Estimation_Imitation_and_Hallucination_Under_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gong_PoseTriplet_Co-Evolving_3D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15625 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_PoseTriplet_Co-Evolving_3D_Human_Pose_Estimation_Imitation_and_Hallucination_Under_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_PoseTriplet_Co-Evolving_3D_Human_Pose_Estimation_Imitation_and_Hallucination_Under_CVPR_2022_paper.html | CVPR 2022 | null |
Injecting Semantic Concepts Into End-to-End Image Captioning | Zhiyuan Fang, Jianfeng Wang, Xiaowei Hu, Lin Liang, Zhe Gan, Lijuan Wang, Yezhou Yang, Zicheng Liu | Tremendous progress has been made in recent years in developing better image captioning models, yet most of them rely on a separate object detector to extract regional features. Recent vision-language studies are shifting towards the detector-free trend by leveraging grid representations for more flexible model training and faster inference speed. However, such development is primarily focused on image understanding tasks, and remains less investigated for the caption generation task. In this paper, we are concerned with a better-performing detector-free image captioning model, and propose a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features. For improved performance, we introduce a novel Concept Token Network (CTN) to predict the semantic concepts and then incorporate them into the end-to-end captioning. In particular, the CTN is built on the basis of a vision transformer and is designed to predict the concept tokens through a classification task, from which the rich semantic information contained greatly benefits the captioning task. Compared with the previous detector-based models, ViTCAP drastically simplifies the architectures and at the same time achieves competitive performance on various challenging image captioning datasets. In particular, ViTCAP reaches 138.1 CIDEr scores on COCO-caption Karpathy-split, 93.8 and 108.6 CIDEr scores on nocaps and Google-CC captioning datasets, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_Injecting_Semantic_Concepts_Into_End-to-End_Image_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fang_Injecting_Semantic_Concepts_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Injecting_Semantic_Concepts_Into_End-to-End_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Injecting_Semantic_Concepts_Into_End-to-End_Image_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
An Efficient Training Approach for Very Large Scale Face Recognition | Kai Wang, Shuo Wang, Panpan Zhang, Zhipeng Zhou, Zheng Zhu, Xiaobo Wang, Xiaojiang Peng, Baigui Sun, Hao Li, Yang You | Face recognition has achieved significant progress in deep learning era due to the ultra-large-scale and welllabeled datasets. However, training on the outsize datasets is time-consuming and takes up a lot of hardware resource. Therefore, designing an efficient training approach is indispensable. The heavy computational and memory costs mainly result from the million-level dimensionality of thefully connected (FC) layer. To this end, we propose a novel training approach, termed Faster Face Classification (F2C), to alleviate time and cost without sacrificing the performance. This method adopts Dynamic Class Pool (DCP) for storing and updating the identities' features dynamically, which could be regarded as a substitute for the FC layer. DCP is efficiently time-saving and cost-saving, as its smaller size with the independence from the whole face identities together. We further validate the proposed F2C method across several face benchmarks and private datasets, and display comparable results, meanwhile the speed is faster than state-of-the-art FC-based methods in terms of recognition accuracy and hardware costs. Moreover, our method is further improved by a well-designed dual data loader including indentity-based and instancebased loaders, which makes it more efficient for the updating DCP parameters. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_An_Efficient_Training_Approach_for_Very_Large_Scale_Face_Recognition_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2105.10375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_An_Efficient_Training_Approach_for_Very_Large_Scale_Face_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_An_Efficient_Training_Approach_for_Very_Large_Scale_Face_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Long-Term Video Frame Interpolation via Feature Propagation | Dawit Mureja Argaw, In So Kweon | Video frame interpolation (VFI) works generally predict intermediate frame(s) by first estimating the motion between inputs and then warping the inputs to the target time with the estimated motion. This approach, however, is not optimal when the temporal distance between the input sequence increases as existing motion estimation modules cannot effectively handle large motions. Hence, VFI works perform well for small frame gaps and perform poorly as the frame gap increases. In this work, we propose a novel framework to address this problem. We argue that when there is a large gap between inputs, instead of estimating imprecise motion that will eventually lead to inaccurate interpolation, we can safely propagate from one side of the input up to a reliable time frame using the other input as a reference. Then, the rest of the intermediate frames can be interpolated using standard approaches as the temporal gap is now narrowed. To this end, we propose a propagation network (PNet) by extending the classic feature-level forecasting with a novel motion-to-feature approach. To be thorough, we adopt a simple interpolation model along with PNet as our full model and design a simple procedure to train the full model in an end-to-end manner. Experimental results on several benchmark datasets confirm the effectiveness of our method for long-term VFI compared to state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Argaw_Long-Term_Video_Frame_Interpolation_via_Feature_Propagation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Argaw_Long-Term_Video_Frame_Interpolation_via_Feature_Propagation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Argaw_Long-Term_Video_Frame_Interpolation_via_Feature_Propagation_CVPR_2022_paper.html | CVPR 2022 | null |
Coarse-To-Fine Q-Attention: Efficient Learning for Visual Robotic Manipulation via Discretisation | Stephen James, Kentaro Wada, Tristan Laidlow, Andrew J. Davison | We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what part of the scene to 'zoom' into. When this 'zooming' behaviour is applied iteratively, it results in a near-lossless discretisation of the translation space, and allows the use of a discrete action, deep Q-learning method. We show that our new coarse-to-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations. | https://openaccess.thecvf.com/content/CVPR2022/papers/James_Coarse-To-Fine_Q-Attention_Efficient_Learning_for_Visual_Robotic_Manipulation_via_Discretisation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/James_Coarse-To-Fine_Q-Attention_Efficient_Learning_for_Visual_Robotic_Manipulation_via_Discretisation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/James_Coarse-To-Fine_Q-Attention_Efficient_Learning_for_Visual_Robotic_Manipulation_via_Discretisation_CVPR_2022_paper.html | CVPR 2022 | null |
Event-Aided Direct Sparse Odometry | Javier Hidalgo-Carrió, Guillermo Gallego, Davide Scaramuzza | We introduce EDS, a direct monocular visual odometry using events and frames. Our algorithm leverages the event generation model to track the camera motion in the blind time between frames. The method formulates a direct probabilistic approach of observed brightness increments. Per-pixel brightness increments are predicted using a sparse number of selected 3D points and are compared to the events via the brightness increment error to estimate camera motion. The method recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the first method to perform 6-DOF VO using events and frames with a direct approach. By design it overcomes the problem of changing appearance in indirect methods. Our results outperform all previous event-based odometry solutions. We also show that, for a target error performance, EDS can work at lower frame rates than state-of-the-art frame-based VO solutions. This opens the door to low-power motion-tracking applications where frames are sparingly triggered "on demand" and our method tracks the motion in between. We release code and datasets to the public. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hidalgo-Carrio_Event-Aided_Direct_Sparse_Odometry_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hidalgo-Carrio_Event-Aided_Direct_Sparse_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hidalgo-Carrio_Event-Aided_Direct_Sparse_Odometry_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hidalgo-Carrio_Event-Aided_Direct_Sparse_Odometry_CVPR_2022_paper.html | CVPR 2022 | null |
Group Contextualization for Video Recognition | Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Xiangnan He | Learning discriminative representation from the complex spatio-temporal dynamic space is essential for video recognition. On top of those stylized spatio-temporal computational units, further refining the learnt feature with axial contexts is demonstrated to be promising in achieving this goal. However, previous works generally focus on utilizing a single kind of contexts to calibrate entire feature channels and could hardly apply to deal with diverse video activities. The problem can be tackled by using pair-wise spatio-temporal attentions to recompute feature response with cross-axis contexts at the expense of heavy computations. In this paper, we propose an efficient feature refinement method that decomposes the feature channels into several groups and separately refines them with different axial contexts in parallel. We refer this lightweight feature calibration as group contextualization (GC). Specifically, we design a family of efficient element-wise calibrators, i.e., ECal-G/S/T/L, where their axial contexts are information dynamics aggregated from other axes either globally or locally, to contextualize feature channel groups. The GC module can be densely plugged into each residual layer of the off-the-shelf video networks. With little computational overhead, consistent improvement is observed when plugging in GC on different networks. By utilizing calibrators to embed feature with four different kinds of contexts in parallel, the learnt representation is expected to be more resilient to diverse types of activities. On videos with rich temporal variations, empirically GC can boost the performance of 2D-CNN (e.g., TSN and TSM) to a level comparable to the state-of-the-art video networks. Code is available at https://github.com/haoyanbin918/Group-Contextualization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hao_Group_Contextualization_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09694 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation | Aming Wu, Cheng Deng | In this paper, we are concerned with enhancing the generalization capability of object detectors. And we consider a realistic yet challenging scenario, namely Single-Domain Generalized Object Detection (Single-DGOD), which aims to learn an object detector that performs well on many unseen target domains with only one source domain for training. Towards Single-DGOD, it is important to extract domain-invariant representations (DIR) containing intrinsical object characteristics, which is beneficial for improving the robustness for unseen domains. Thus, we present a method, i.e., cyclic-disentangled self-distillation, to disentangle DIR from domain-specific representations without the supervision of domain-related annotations (e.g., domain labels). Concretely, a cyclic-disentangled module is first proposed to cyclically extract DIR from the input visual features. Through the cyclic operation, the disentangled ability can be promoted without the reliance on domain-related annotations. Then, taking the DIR as the teacher, we design a self-distillation module to further enhance the generalization ability. In the experiments, our method is evaluated in urban-scene object detection. Experimental results of five weather conditions show that our method obtains a significant performance gain over baseline methods. Particularly, for the night-sunny scene, our method outperforms baselines by 3%, which indicates that our method is instrumental in enhancing generalization ability. Data and code are available at https://github.com/AmingWu/Single-DGOD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Single-Domain_Generalized_Object_Detection_in_Urban_Scene_via_Cyclic-Disentangled_Self-Distillation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Single-Domain_Generalized_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Single-Domain_Generalized_Object_Detection_in_Urban_Scene_via_Cyclic-Disentangled_Self-Distillation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Single-Domain_Generalized_Object_Detection_in_Urban_Scene_via_Cyclic-Disentangled_Self-Distillation_CVPR_2022_paper.html | CVPR 2022 | null |
Visual Abductive Reasoning | Chen Liang, Wenguan Wang, Tianfei Zhou, Yi Yang | Abductive reasoning seeks the likeliest possible explanation for partial observations. Although abduction is frequently employed in human daily reasoning, it is rarely explored in computer vision literature. In this paper, we propose a new task and dataset, Visual Abductive Reasoning (VAR), for examining abductive reasoning ability of machine intelligence in everyday visual situations. Given an incomplete set of visual events, AI systems are required to not only describe what is observed, but also infer the hypothesis that can best explain the visual premise. Based on our large-scale VAR dataset, we devise a strong baseline model, Reasoner (causal-and-cascaded reasoning Transformer). First, to capture the causal structure of the observations, a contextualized directional position embedding strategy is adopted in the encoder, that yields discriminative representations for the premise and hypothesis. Then, multiple decoders are cascaded to generate and progressively refine the premise and hypothesis sentences. The prediction scores of the sentences are used to guide cross-sentence information flow in the cascaded reasoning procedure. Our VAR benchmarking results show that Reasoner surpasses many famous video-language models, while still being far behind human performance. This work is expected to foster future efforts in the reasoning-beyond-observation paradigm. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Visual_Abductive_Reasoning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Visual_Abductive_Reasoning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14040 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Visual_Abductive_Reasoning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Visual_Abductive_Reasoning_CVPR_2022_paper.html | CVPR 2022 | null |
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation | Peng-Tao Jiang, Yuqi Yang, Qibin Hou, Yunchao Wei | Mining precise class-aware attention maps, a.k.a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. We observe that classification models can discover object regions with more details when replacing the input image with its local patches. Taking this into account, we first leverage a local classification network to extract attentions from multiple local patches randomly cropped from the input image. Then, we utilize a global network to learn complementary attention knowledge across multiple local attention maps online. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014, respectively, setting new state-of-the-art records. Code is available at https://github.com/PengtaoJiang/L2G. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_L2G_A_Simple_Local-to-Global_Knowledge_Transfer_Framework_for_Weakly_Supervised_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.03206 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_L2G_A_Simple_Local-to-Global_Knowledge_Transfer_Framework_for_Weakly_Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_L2G_A_Simple_Local-to-Global_Knowledge_Transfer_Framework_for_Weakly_Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation | Jianfeng Wang, Thomas Lukasiewicz | Recently, several Bayesian deep learning methods have been proposed for semi-supervised medical image segmentation. Although they have achieved promising results on medical benchmarks, some problems are still existing. Firstly, their overall architectures belong to the discriminative models, and hence, in the early stage of training, they only use labeled data for training, which might make them overfit to the labeled data. Secondly, in fact, they are only partially based on Bayesian deep learning, as their overall architectures are not designed under the Bayesian framework. However, unifying the overall architecture under the Bayesian perspective can make the architecture have a rigorous theoretical basis, so that each part of the architecture can have a clear probabilistic interpretation. Therefore, to solve the problems, we propose a new generative Bayesian deep learning (GBDL) architecture. GBDL belongs to the generative models, whose target is to estimate the joint distribution of input medical volumes and their corresponding labels. Estimating the joint distribution implicitly involves the distribution of data, so both labeled and unlabeled data can be utilized in the early stage of training, which alleviates the potential overfitting problem. Besides, GBDL is completely designed under the Bayesian framework, and thus we give its full Bayesian formulation, which lays a theoretical probabilistic foundation for our architecture. Extensive experiments show that our GBDL outperforms previous state-of-the-art methods in terms of four commonly used evaluation indicators on three public medical datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Rethinking_Bayesian_Deep_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Continual Learning With Lifelong Vision Transformer | Zhen Wang, Liu Liu, Yiqun Duan, Yajing Kong, Dacheng Tao | Continual learning methods aim at training a neural network from sequential data with streaming labels, relieving catastrophic forgetting. However, existing methods are based on and designed for convolutional neural networks (CNNs), which have not utilized the full potential of newly emerged powerful vision transformers. In this paper, we propose a novel attention-based framework Lifelong Vision Transformer (LVT), to achieve a better stability-plasticity trade-off for continual learning. Specifically, an inter-task attention mechanism is presented in LVT, which implicitly absorbs the previous tasks' information and slows down the drift of important attention between previous tasks and the current task. LVT designs a dual-classifier structure that independently injects new representation to avoid catastrophic interference and accumulates the new and previous knowledge in a balanced manner to improve the overall performance. Moreover, we develop a confidence-aware memory update strategy to deepen the impression of the previous tasks. The extensive experimental results show that our approach achieves state-of-the-art performance with even fewer parameters on continual learning benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Continual_Learning_With_Lifelong_Vision_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Continual_Learning_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Continual_Learning_With_Lifelong_Vision_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Continual_Learning_With_Lifelong_Vision_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
MPViT: Multi-Path Vision Transformer for Dense Prediction | Youngwan Lee, Jonghee Kim, Jeffrey Willette, Sung Ju Hwang | Dense computer vision tasks such as object detection and segmentation require effective multi-scale feature representation for detecting or classifying objects or regions with varying sizes. While Convolutional Neural Networks (CNNs) have been the dominant architectures for such tasks, recently introduced Vision Transformers (ViTs) aim to replace them as a backbone. Similar to CNNs, ViTs build a simple multi-stage structure (i.e., fine-to-coarse) for multi-scale representation with single-scale patches. In this work, with a different perspective from existing Transformers, we explore multi-scale patch embedding and multi-path structure, constructing the Multi-Path Vision Transformer (MPViT). MPViT embeds features of the same size (i.e., sequence length) with patches of different scales simultaneously by using overlapping convolutional patch embedding. Tokens of different scales are then independently fed into the Transformer encoders via multiple paths and the resulting features are aggregated, enabling both fine and coarse feature representations at the same feature level. Thanks to the diverse, multi-scale feature representations, our MPViTs scaling from tiny (5M) to base (73M) consistently achieve superior performance over state-of-the-art Vision Transformers on ImageNet classification, object detection, instance segmentation, and semantic segmentation. These extensive results demonstrate that MPViT can serve as a versatile backbone network for various vision tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_MPViT_Multi-Path_Vision_Transformer_for_Dense_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_MPViT_Multi-Path_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11010 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MPViT_Multi-Path_Vision_Transformer_for_Dense_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MPViT_Multi-Path_Vision_Transformer_for_Dense_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models | Simin Chen, Zihe Song, Mirazul Haque, Cong Liu, Wei Yang | Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work focuses on improving NICG model accuracy while efficiency is less explored. However, many real-world applications require real-time feedback, which highly relies on the efficiency of NICG models. Recent research observed that the efficiency of NICG models could vary for different inputs. This observation brings in a new attack surface of NICG models, i.e., An adversary might be able to slightly change inputs to cause the NICG models to consume more computational resources. To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models. Our experimental results show that NICGSlowDown can generate images with human-unnoticeable perturbations that will increase the NICG model latency up to 483.86%. We hope this research could raise the community's concern about the efficiency robustness of NICG models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_NICGSlowDown_Evaluating_the_Efficiency_Robustness_of_Neural_Image_Caption_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_NICGSlowDown_Evaluating_the_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_NICGSlowDown_Evaluating_the_Efficiency_Robustness_of_Neural_Image_Caption_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_NICGSlowDown_Evaluating_the_Efficiency_Robustness_of_Neural_Image_Caption_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation | Shreyas Hampali, Sayan Deb Sarkar, Mahdi Rad, Vincent Lepetit | We propose a robust and accurate method for estimating the 3D poses of two hands in close interaction from a single color image. This is a very challenging problem, as large occlusions and many confusions between the joints may happen. State-of-the-art methods solve this problem by regressing a heatmap for each joint, which requires solving two problems simultaneously: localizing the joints and recognizing them. In this work, we propose to separate these tasks by relying on a CNN to first localize joints as 2D keypoints, and on self-attention between the CNN features at these keypoints to associate them with the corresponding hand joint. The resulting architecture, which we call "Keypoint Transformer", is highly efficient as it achieves state-of-the-art performance with roughly half the number of model parameters on the InterHand2.6M dataset. We also show it can be easily extended to estimate the 3D pose of an object manipulated by one or two hands with high performance. Moreover, we created a new dataset of more than 75,000 images of two hands manipulating an object fully annotated in 3D and will make it publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hampali_Keypoint_Transformer_Solving_Joint_Identification_in_Challenging_Hands_and_Object_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hampali_Keypoint_Transformer_Solving_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.14639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hampali_Keypoint_Transformer_Solving_Joint_Identification_in_Challenging_Hands_and_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hampali_Keypoint_Transformer_Solving_Joint_Identification_in_Challenging_Hands_and_Object_CVPR_2022_paper.html | CVPR 2022 | null |
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing | Yichun Shi, Xiao Yang, Yangyue Wan, Xiaohui Shen | Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However, since the latent codes of StyleGANs are designed to control global styles, it is hard to achieve a fine-grained control over synthesized images. We present SemanticStyleGAN, where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way. The structure and texture of different local parts are controlled by corresponding latent codes. Experimental results demonstrate that our model provides a strong disentanglement between different spatial areas. When combined with editing methods designed for StyleGANs, it can achieve a more fine-grained control to edit synthesized or real images. The model can also be extended to other domains via transfer learning. Thus, as a generic prior model with built-in disentanglement, it could facilitate the development of GAN-based applications and enable more potential downstream tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_SemanticStyleGAN_Learning_Compositional_Generative_Priors_for_Controllable_Image_Synthesis_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_SemanticStyleGAN_Learning_Compositional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02236 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_SemanticStyleGAN_Learning_Compositional_Generative_Priors_for_Controllable_Image_Synthesis_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_SemanticStyleGAN_Learning_Compositional_Generative_Priors_for_Controllable_Image_Synthesis_and_CVPR_2022_paper.html | CVPR 2022 | null |
Accurate 3D Body Shape Regression Using Metric and Semantic Attributes | Vasileios Choutas, Lea Müller, Chun-Hao P. Huang, Siyu Tang, Dimitrios Tzionas, Michael J. Black | While methods that regress 3D human meshes from images have progressed rapidly, the estimated body shapes often do not capture the true human shape. This is problematic since, for many applications, accurate body shape is as important as pose. The key reason that body shape accuracy lags pose accuracy is the lack of data. While humans can label 2D joints, and these constrain 3D pose, it is not so easy to "label" 3D body shape. Since paired data with images and 3D body shape are rare, we exploit two sources of information: (1) we collect internet images of diverse "fashion" models together with a small set of anthropometric measurements; (2) we collect linguistic shape attributes for a wide range of 3D body meshes and the model images. Taken together, these datasets provide sufficient constraints to infer dense 3D shape. We exploit the anthropometric measurements and linguistic shape attributes in several novel ways to train a neural network, called SHAPY, that regresses 3D human pose and shape from an RGB image. We evaluate SHAPY on public benchmarks, but note that they either lack significant body shape variation, ground-truth shape, or clothing variation. Thus, we collect a new dataset for evaluating 3D human shape estimation, called HBW, containing photos of "Human Bodies in the Wild" for which we have ground-truth 3D body scans. On this new benchmark, SHAPY significantly outperforms state-of-the-art methods on the task of 3D body shape estimation. This is the first demonstration that 3D body shape regression from images can be trained from easy-to-obtain anthropometric measurements and linguistic shape attributes. Our model and data are available at: shapy.is.tue.mpg.de | https://openaccess.thecvf.com/content/CVPR2022/papers/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Choutas_Accurate_3D_Body_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.html | CVPR 2022 | null |
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers | Estelle Aflalo, Meng Du, Shao-Yen Tseng, Yongfei Liu, Chenfei Wu, Nan Duan, Vasudev Lal | Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability tools have become available for NLP models, internal mechanisms of vision and multimodal transformers remain largely opaque. With the success of these transformers, it is increasingly critical to understand their inner workings, as unraveling these black-boxes will lead to more capable and trustworthy models. To contribute to this quest, we propose VL-InterpreT, which provides novel interactive visualizations for interpreting the attentions and hidden representations in multimodal transformers. VL-InterpreT is a task agnostic and integrated tool that (1) tracks a variety of statistics in attention heads throughout all layers for both vision and language components, (2) visualizes cross-modal and intra-modal attentions through easily readable heatmaps, and (3) plots the hidden representations of vision and language tokens as they pass through the transformer layers. In this paper, we demonstrate the functionalities of VL-InterpreT through the analysis of KD-VLP, an end-to-end pretraining vision-language multimodal transformer-based model, in the tasks of Visual Commonsense Reasoning (VCR) and WebQA, two visual question answering benchmarks. Furthermore, we also present a few interesting findings about multimodal transformer behaviors that were learned through our tool. | https://openaccess.thecvf.com/content/CVPR2022/papers/Aflalo_VL-InterpreT_An_Interactive_Visualization_Tool_for_Interpreting_Vision-Language_Transformers_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aflalo_VL-InterpreT_An_Interactive_Visualization_Tool_for_Interpreting_Vision-Language_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aflalo_VL-InterpreT_An_Interactive_Visualization_Tool_for_Interpreting_Vision-Language_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Label-Only Model Inversion Attacks via Boundary Repulsion | Mostafa Kahla, Si Chen, Hoang Anh Just, Ruoxi Jia | Recent studies show that the state-of-the-art deep neural networks are vulnerable to model inversion attacks, in which access to a model is abused to reconstruct private training data of any given target class. Existing attacks rely on having access to either the complete target model(whitebox) or the model's soft-labels (blackbox). However,no prior work has been done in the harder but more practical scenario, in which the attacker only has access to the model's predicted label, without a confidence measure. In this paper, we introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert private training data using only the target model's predicted labels. The key idea of our algorithm is to evaluate the model's predicted labels over a sphere and then estimate the direction to reach the target class's centroid. Using the example of face recognition, we show that the images reconstructed by BREP-MI successfully reproduce the semantics of the private training data for various datasets and target model architectures. We compare BREP-MI with the state-of-the-art white-box and blackbox model inversion attacks and the results show that despite assuming less knowledge about the target model, BREP-MI outperforms the blackbox attack and achieves comparable results to the whitebox attack. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kahla_Label-Only_Model_Inversion_Attacks_via_Boundary_Repulsion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kahla_Label-Only_Model_Inversion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01925 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kahla_Label-Only_Model_Inversion_Attacks_via_Boundary_Repulsion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kahla_Label-Only_Model_Inversion_Attacks_via_Boundary_Repulsion_CVPR_2022_paper.html | CVPR 2022 | null |
Privacy-Preserving Online AutoML for Domain-Specific Face Detection | Chenqian Yan, Yuge Zhang, Quanlu Zhang, Yaming Yang, Xinyang Jiang, Yuqing Yang, Baoyuan Wang | Despite the impressive progress of general face detection, the tuning of hyper-parameters and architectures is still critical for the performance of a domain-specific face detector. Though existing AutoML works can speedup such process, they either require tuning from scratch for a new scenario or do not consider data privacy. To scale up, we derive a new AutoML setting from a platform perspective. In such setting, new datasets sequentially arrive at the platform, where an architecture and hyper-parameter configuration is recommended to train the optimal face detector for each dataset. This, however, brings two major challenges: (1) how to predict the best configuration for any given dataset without touching their raw images due to the privacy concern? and (2) how to continuously improve the AutoML algorithm from previous tasks and offer a better warm-up for future ones? We introduce "HyperFD", a new privacy-preserving online AutoML framework for face detection. At its core part, a novel meta-feature representation of a dataset as well as its learning paradigm is proposed. Thanks to HyperFD, each local task (client) is able to effectively leverage the learning "experience" of previous tasks without uploading raw images to the platform; meanwhile, the meta-feature extractor is continuously learned to better trade off the bias and variance. Extensive experiments demonstrate the effectiveness and efficiency of our design. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_Privacy-Preserving_Online_AutoML_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08399 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Augmented Unpaired Image Dehazing via Density and Depth Decomposition | Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, Dacheng Tao | To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image pairs, many recent methods attempted to improve models' generalization ability by training on unpaired data. Most of them simply formulate dehazing and rehazing cycles, yet ignore the physical properties of the real-world hazy environment, i.e. the haze varies with density and depth. In this paper, we propose a self-augmented image dehazing framework, termed D^4 (Dehazing via Decomposing transmission map into Density and Depth) for haze generation and removal. Instead of merely estimating transmission maps or clean content, the proposed framework focuses on exploring scattering coefficient and depth information contained in hazy and clean images. With estimated scene depth, our method is capable of re-rendering hazy images with different thicknesses which further benefits the training of the dehazing network. It is worth noting that the whole training process needs only unpaired hazy and clean images, yet succeeded in recovering the scattering coefficient, depth map and clean content from a single hazy image. Comprehensive experiments demonstrate our method outperforms state-of-the-art unpaired dehazing methods with much fewer parameters and FLOPs. Our code is available at https://github.com/YaN9-Y/D4 | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Self-Augmented_Unpaired_Image_Dehazing_via_Density_and_Depth_Decomposition_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Self-Augmented_Unpaired_Image_Dehazing_via_Density_and_Depth_Decomposition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Self-Augmented_Unpaired_Image_Dehazing_via_Density_and_Depth_Decomposition_CVPR_2022_paper.html | CVPR 2022 | null |
Neural 3D Video Synthesis From Multi-View Video | Tianye Li, Mira Slavcheva, Michael Zollhöfer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, Zhaoyang Lv | We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance field that represents scene dynamics using a set of compact latent codes. We are able to significantly boost the training speed and perceptual quality of the generated imagery by a novel hierarchical training scheme in combination with ray importance sampling. Our learned representation is highly compact and able to represent a 10 second 30 FPS multi-view video recording by 18 cameras with a model size of only 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the state of the art. Project website: https://neural-3d-video.github.io/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Neural_3D_Video_Synthesis_From_Multi-View_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Neural_3D_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Neural_3D_Video_Synthesis_From_Multi-View_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Neural_3D_Video_Synthesis_From_Multi-View_Video_CVPR_2022_paper.html | CVPR 2022 | null |
LiDAR Snowfall Simulation for Robust 3D Object Detection | Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, Luc Van Gool | 3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of LiDAR-based 3D object detection under snowfall. Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds. Our method samples snow particles in 2D space for each LiDAR line and uses the induced geometry to modify the measurement for each LiDAR beam accordingly. Moreover, as snowfall often causes wetness on the ground, we also simulate ground wetness on LiDAR point clouds. We use our simulation to generate partially synthetic snowy LiDAR data and leverage these data for training 3D object detection models that are robust to snowfall. We conduct an extensive evaluation using several state-of-the-art 3D object detection methods and show that our simulation consistently yields significant performance gains on the real snowy STF dataset compared to clear-weather baselines and competing simulation approaches, while not sacrificing performance in clear weather. Our code is available at github.com/SysCV/LiDAR_snow_sim. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hahner_LiDAR_Snowfall_Simulation_for_Robust_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hahner_LiDAR_Snowfall_Simulation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15118 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hahner_LiDAR_Snowfall_Simulation_for_Robust_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hahner_LiDAR_Snowfall_Simulation_for_Robust_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Where To Learn in Cross-View Self-Supervised Learning | Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Toshihiko Yamasaki | Self-supervised learning (SSL) has made enormous progress and largely narrowed the gap with the supervised ones, where the representation learning is mainly guided by a projection into an embedding space. During the projection, current methods simply adopt uniform aggregation of pixels for embedding; however, this risks involving object-irrelevant nuisances and spatial misalignment for different augmentations. In this paper, we present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features, so that the projected embeddings could be exactly aligned and thus guide the feature learning better. Concretely, we reinterpret the projection head in SSL as a per-pixel projection and predict a set of spatial alignment maps from the original features by this weight-sharing projection head. A spectrum of aligned embeddings is thus obtained by aggregating the features with spatial weighting according to these alignment maps. As a result of this adaptive alignment, we observe substantial improvements on both image-level prediction and dense prediction at the same time: LEWEL improves MoCov2 by 1.6%/1.3%/0.5%/0.4% points, improves BYOL by 1.3%/1.3%/0.7%/0.6% points, on ImageNet linear/semi-supervised classification, Pascal VOC semantic segmentation, and object detection, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Learning_Where_To_Learn_in_Cross-View_Self-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Learning_Where_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14898 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Learning_Where_To_Learn_in_Cross-View_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Learning_Where_To_Learn_in_Cross-View_Self-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation | Ziyi Wang, Yongming Rao, Xumin Yu, Jie Zhou, Jiwen Lu | Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on these class-agnostic local geometric representations may raise confusion between local parts from different categories that are similar in appearance or spatially adjacent. To address this issue, we argue that mid-level features can be further enhanced with semantic information, and propose semantic-affine transformation that transforms features of mid-level points belonging to different categories with class-specific affine parameters. Based on this technique, we propose SemAffiNet for point cloud semantic segmentation, which utilizes the attention mechanism in the Transformer module to implicitly and explicitly capture global structural knowledge within local parts for overall comprehension of each category. We conduct extensive experiments on the ScanNetV2 and NYUv2 datasets, and evaluate semantic-affine transformation on various 3D point cloud and 2D image segmentation baselines, where both qualitative and quantitative results demonstrate the superiority and generalization ability of our proposed approach. Code is available at https://github.com/wangzy22/SemAffiNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_SemAffiNet_Semantic-Affine_Transformation_for_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_SemAffiNet_Semantic-Affine_Transformation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.13490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_SemAffiNet_Semantic-Affine_Transformation_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_SemAffiNet_Semantic-Affine_Transformation_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Sparse Object-Level Supervision for Instance Segmentation With Pixel Embeddings | Adrian Wolny, Qin Yu, Constantin Pape, Anna Kreshuk | Most state-of-the-art instance segmentation methods have to be trained on densely annotated images. While difficult in general, this requirement is especially daunting for biomedical images, where domain expertise is often required for annotation and no large public data collections are available for pre-training. We propose to address the dense annotation bottleneck by introducing a proposal-free segmentation approach based on non-spatial embeddings, which exploits the structure of the learned embedding space to extract individual instances in a differentiable way. The segmentation loss can then be applied directly to instances and the overall pipeline can be trained in a fully- or weakly supervised manner. We consider the challenging case of positive-unlabeled supervision, where a novel self-supervised consistency loss is introduced for the unlabeled parts of the training data. We evaluate the proposed method on 2D and 3D segmentation problems in different microscopy modalities as well as on the Cityscapes and CVPPP instance segmentation benchmarks, achieving state-of-the-art results on the latter. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wolny_Sparse_Object-Level_Supervision_for_Instance_Segmentation_With_Pixel_Embeddings_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wolny_Sparse_Object-Level_Supervision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.14572 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wolny_Sparse_Object-Level_Supervision_for_Instance_Segmentation_With_Pixel_Embeddings_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wolny_Sparse_Object-Level_Supervision_for_Instance_Segmentation_With_Pixel_Embeddings_CVPR_2022_paper.html | CVPR 2022 | null |
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks | Rafid Mahmood, James Lucas, David Acuna, Daiqing Li, Jonah Philion, Jose M. Alvarez, Zhiding Yu, Sanja Fidler, Marc T. Law | Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance in applications such as autonomous driving or medical imaging where collecting data is expensive and time-consuming. Overestimating or underestimating data requirements incurs substantial costs that could be avoided with an adequate budget. Prior work on neural scaling laws suggest that the power-law function can fit the validation performance curve and extrapolate it to larger data set sizes. We find that this does not immediately translate to the more difficult downstream task of estimating the required data set size to meet a target performance. In this work, we consider a broad class of computer vision tasks and systematically investigate a family of functions that generalize the power-law function to allow for better estimation of data requirements. Finally, we show that incorporating a tuned correction factor and collecting over multiple rounds significantly improves the performance of the data estimators. Using our guidelines, practitioners can accurately estimate data requirements of machine learning systems to gain savings in both development time and data acquisition costs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mahmood_How_Much_More_Data_Do_I_Need_Estimating_Requirements_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mahmood_How_Much_More_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mahmood_How_Much_More_Data_Do_I_Need_Estimating_Requirements_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mahmood_How_Much_More_Data_Do_I_Need_Estimating_Requirements_for_CVPR_2022_paper.html | CVPR 2022 | null |
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation | Deyi Ji, Haoran Wang, Mingyuan Tao, Jianqiang Huang, Xian-Sheng Hua, Hongtao Lu | Existing knowledge distillation works for semantic segmentation mainly focus on transfering high-level contextual knowledge from teacher to student. However, low-level texture knowledge is also of vital importance for characterizing the local structural pattern and global statistical property, such as boundary, smoothness, regularity and color contrast, which may not be well addressed by high-level deep features. In this paper, we are intended to take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for Semantic Segmentation. Specifically, for structural texture knowledge, we introduce a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative laplacian pyramid and directional filter bank to mine the structural texture knowledge. For statistical knowledge, we propose a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation. Finally, each knowledge learning is supervised by an individual loss function, forcing the student network to mimic the teacher better from a broader perspective. Experiments show that the proposed method achieves state-of-the-art performance on Cityscapes, Pascal VOC 2012 and ADE20K datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ji_Structural_and_Statistical_Texture_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ji_Structural_and_Statistical_Texture_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ji_Structural_and_Statistical_Texture_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search | Han Xiao, Ziwei Wang, Zheng Zhu, Jie Zhou, Jiwen Lu | In this paper, we propose a Shapley value based method to evaluate operation contribution (Shapley-NAS) for neural architecture search. Differentiable architecture search (DARTS) acquires the optimal architectures by optimizing the architecture parameters with gradient descent, which significantly reduces the search cost. However, the magnitude of architecture parameters updated by gradient descent fails to reveal the actual operation importance to the task performance and therefore harms the effectiveness of obtained architectures. By contrast, we propose to evaluate the direct influence of operations on validation accuracy. To deal with the complex relationships between supernet components, we leverage Shapley value to quantify their marginal contributions by considering all possible combinations. Specifically, we iteratively optimize the supernet weights and update the architecture parameters by evaluating operation contributions via Shapley value, so that the optimal architectures are derived by selecting the operations that contribute significantly to the tasks. Since the exact computation of Shapley value is NP-hard, the Monte-Carlo sampling based algorithm with early truncation is employed for efficient approximation, and the momentum update mechanism is adopted to alleviate fluctuation of the sampling process. Extensive experiments on various datasets and various search spaces show that our Shapley-NAS outperforms the state-of-the-art methods by a considerable margin with light search cost. The code is available at https://github.com/Euphoria16/Shapley-NAS.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiao_Shapley-NAS_Discovering_Operation_Contribution_for_Neural_Architecture_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiao_Shapley-NAS_Discovering_Operation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Shapley-NAS_Discovering_Operation_Contribution_for_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Shapley-NAS_Discovering_Operation_Contribution_for_Neural_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
The Implicit Values of a Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement | Ilya Chugunov, Yuxuan Zhang, Zhihao Xia, Xuaner Zhang, Jiawen Chen, Felix Heide | Modern smartphones can continuously stream multi-megapixel RGB images at 60Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates. During a snapshot photograph, the natural unsteadiness of the photographer's hands offers millimeter-scale variation in camera pose, which we can capture along with RGB and depth in a circular buffer. In this work we explore how, from a bundle of these measurements acquired during viewfinding, we can combine dense micro-baseline parallax cues with kilopixel LiDAR depth to distill a high-fidelity depth map. We take a test-time optimization approach and train a coordinate MLP to output photometrically and geometrically consistent depth estimates at the continuous coordinates along the path traced by the photographer's natural hand shake. With no additional hardware, artificial hand motion, or user interaction beyond the press of a button, our proposed method brings high-resolution depth estimates to point-and-shoot "tabletop" photography -- textured objects at close range. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chugunov_The_Implicit_Values_of_a_Good_Hand_Shake_Handheld_Multi-Frame_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chugunov_The_Implicit_Values_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13738 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chugunov_The_Implicit_Values_of_a_Good_Hand_Shake_Handheld_Multi-Frame_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chugunov_The_Implicit_Values_of_a_Good_Hand_Shake_Handheld_Multi-Frame_CVPR_2022_paper.html | CVPR 2022 | null |
Learning What Not To Segment: A New Perspective on Few-Shot Segmentation | Chunbo Lang, Gong Cheng, Binfei Tu, Junwei Han | Recently few-shot segmentation (FSS) has been extensively developed. Most previous works strive to achieve generalization through the meta-learning framework derived from classification tasks; however, the trained models are biased towards the seen classes instead of being ideally class-agnostic, thus hindering the recognition of new concepts. This paper proposes a fresh and straightforward insight to alleviate the problem. Specifically, we apply an additional branch (base learner) to the conventional FSS model (meta learner) to explicitly identify the targets of base classes, i.e., the regions that do not need to be segmented. Then, the coarse results output by these two learners in parallel are adaptively integrated to yield precise segmentation prediction. Considering the sensitivity of meta learner, we further introduce an adjustment factor to estimate the scene differences between the input image pairs for facilitating the model ensemble forecasting. The substantial performance gains on PASCAL-5i and COCO-20i verify the effectiveness, and surprisingly, our versatile scheme sets a new state-of-the-art even with two plain learners. Moreover, in light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting, i.e., generalized FSS, where the pixels of both base and novel classes are required to be determined. The source code is available at github.com/chunbolang/BAM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lang_Learning_What_Not_To_Segment_A_New_Perspective_on_Few-Shot_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lang_Learning_What_Not_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lang_Learning_What_Not_To_Segment_A_New_Perspective_on_Few-Shot_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lang_Learning_What_Not_To_Segment_A_New_Perspective_on_Few-Shot_CVPR_2022_paper.html | CVPR 2022 | null |
Blended Diffusion for Text-Driven Editing of Natural Images | Omri Avrahami, Dani Lischinski, Ohad Fried | Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions of the input image with the local text-guided diffusion latent at a progression of noise levels. In addition, we show that adding augmentations to the diffusion process mitigates adversarial results. We compare against several baselines and related methods, both qualitatively and quantitatively, and show that our method outperforms these solutions in terms of overall realism, ability to preserve the background and matching the text. Finally, we show several text-driven editing applications, including adding a new object to an image, removing/replacing/altering existing objects, background replacement, and image extrapolation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Avrahami_Blended_Diffusion_for_Text-Driven_Editing_of_Natural_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Avrahami_Blended_Diffusion_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Avrahami_Blended_Diffusion_for_Text-Driven_Editing_of_Natural_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Avrahami_Blended_Diffusion_for_Text-Driven_Editing_of_Natural_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Unsupervised Domain Generalization | Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, Haoxin Liu | Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains. The performances of current DG methods largely rely on sufficient labeled data, which are usually costly or unavailable, however. Since unlabeled data are far more accessible, we seek to explore how unsupervised learning can help deep models generalize across domains. Specifically, we study a novel generalization problem called unsupervised domain generalization (UDG), which aims to learn generalizable models with unlabeled data and analyze the effects of pre-training on DG. In UDG, models are pretrained with unlabeled data from various source domains before being trained on labeled source data and eventually tested on unseen target domains. Then we propose a method named Domain-Aware Representation LearnING (DARLING) to cope with the significant and misleading heterogeneity within unlabeled pretraining data and severe distribution shifts between source and target data. Surprisingly we observe that DARLING can not only counterbalance the scarcity of labeled data but also further strengthen the generalization ability of models when the labeled data are insufficient. As a pretraining approach, DARLING shows superior or comparable performance compared with ImageNet pretraining protocol even when the available data are unlabeled and of a vastly smaller amount compared to ImageNet, which may shed light on improving generalization with large-scale unlabeled data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Towards_Unsupervised_Domain_Generalization_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2107.06219 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Towards_Unsupervised_Domain_Generalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Towards_Unsupervised_Domain_Generalization_CVPR_2022_paper.html | CVPR 2022 | null |
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening | Wele Gedara Chaminda Bandara, Vishal M. Patel | Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. In this paper, we present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively. HyperTransformer consists of three main modules, namely two separate feature extractors for PAN and HSI, a multi-head feature soft attention module, and a spatial-spectral feature fusion module. Such a network improves both spatial and spectral quality measures of the pansharpened HSI by learning cross-feature space dependencies and long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be utilized across multiple spatial scales at the backbone for obtaining improved performance. Extensive experiments conducted on three widely used datasets demonstrate that HyperTransformer achieves significant improvement over the state-of-the-art methods on both spatial and spectral quality measures. Implementation code and pre-trained weights can be accessed at https://github.com/wgcban/HyperTransformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bandara_HyperTransformer_A_Textural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02503 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.html | CVPR 2022 | null |
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation | Anirud Thyagharajan, Benjamin Ummenhofer, Prashant Laddha, Om Ji Omer, Sreenivas Subramoney | 3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art semantic segmentation models suffer from the part-misclassification problem, wherein parts of the same object are labelled incorrectly. Previous methods have utilized hierarchical, iterative methods to fuse semantic and instance information, but they lack learnability in context fusion, and are computationally complex and heuristic driven. This paper presents Segment-Fusion, a novel attention-based method for hierarchical fusion of semantic and instance information to address the part misclassifications. The presented method includes a graph segmentation algorithm for grouping points into segments that pools point-wise features into segment-wise features, a learnable attention-based network to fuse these segments based on their semantic and instance features, and followed by a simple yet effective connected component labelling algorithm to convert segment features to instance labels. Segment-Fusion can be flexibly employed with any network architecture for semantic/instance segmentation. It improves the qualitative and quantitative performance of several semantic segmentation backbones by upto 5% on the ScanNet and S3DIS datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Thyagharajan_Segment-Fusion_Hierarchical_Context_Fusion_for_Robust_3D_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Thyagharajan_Segment-Fusion_Hierarchical_Context_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Thyagharajan_Segment-Fusion_Hierarchical_Context_Fusion_for_Robust_3D_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Thyagharajan_Segment-Fusion_Hierarchical_Context_Fusion_for_Robust_3D_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Invertible Image Steganography | Youmin Xu, Chong Mou, Yujie Hu, Jingfen Xie, Jian Zhang | Image steganography aims to hide secret images into a container image, where the secret is hidden from human vision and can be restored when necessary. Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Gaussian noise, Poisson noise, and lossy compression. This paper presents a novel flow-based framework for robust invertible image steganography, dubbed as RIIS. We introduce the conditional normalizing flow to model the distribution of the redundant high-frequency component with the condition of the container image. Moreover, a well-designed container enhancement module (CEM) also contributes to the robust reconstruction. To regulate the network parameters for different distortion levels, we propose a distortion-guided modulation (DGM) over flow-based blocks to make it a one-size-fits-all model. In terms of both clean and distorted image steganography, extensive experiments reveal that the proposed RIIS efficiently improves the robustness while maintaining imperceptibility and capacity. As far as we know, we are the first learning-based scheme to enhance the robustness of image steganography in the literature. The guarantee of steganography robustness significantly broadens the application of steganography in real-world applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Robust_Invertible_Image_Steganography_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Robust_Invertible_Image_Steganography_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Robust_Invertible_Image_Steganography_CVPR_2022_paper.html | CVPR 2022 | null |
Entropy-Based Active Learning for Object Detection With Progressive Diversity Constraint | Jiaxi Wu, Jiaxin Chen, Di Huang | Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label. Active learning for object detection is more challenging and existing efforts on it are relatively rare. In this paper, we propose a novel hybrid approach to address this problem, where the instance-level uncertainty and diversity are jointly considered in a bottom-up manner. To balance the computational complexity, the proposed approach is designed as a two-stage procedure. At the first stage, an Entropy-based Non-Maximum Suppression (ENMS) is presented to estimate the uncertainty of every image, which performs NMS according to the entropy in the feature space to remove predictions with redundant information gains. At the second stage, a diverse prototype (DivProto) strategy is explored to ensure the diversity across images by progressively converting it into the intra-class and inter-class diversities of the entropy-based class-specific prototypes. Extensive experiments are conducted on MS COCO and Pascal VOC, and the proposed approach achieves state of the art results and significantly outperforms the other counterparts, highlighting its superiority. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Entropy-Based_Active_Learning_for_Object_Detection_With_Progressive_Diversity_Constraint_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Entropy-Based_Active_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Entropy-Based_Active_Learning_for_Object_Detection_With_Progressive_Diversity_Constraint_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Entropy-Based_Active_Learning_for_Object_Detection_With_Progressive_Diversity_Constraint_CVPR_2022_paper.html | CVPR 2022 | null |
BE-STI: Spatial-Temporal Integrated Network for Class-Agnostic Motion Prediction With Bidirectional Enhancement | Yunlong Wang, Hongyu Pan, Jun Zhu, Yu-Huan Wu, Xin Zhan, Kun Jiang, Diange Yang | Determining the motion behavior of inexhaustible categories of traffic participants is critical for autonomous driving. In recent years, there has been a rising concern in performing class-agnostic motion prediction directly from the captured sensor data, like LiDAR point clouds or the combination of point clouds and images. Current motion prediction frameworks tend to perform joint semantic segmentation and motion prediction and face the trade-off between the performance of these two tasks. In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction. Specifically, we propose to enhance the spatial features of each individual point cloud with the similarity among temporal neighboring frames and enhance the global temporal features with the spatial difference among non-adjacent frames in a coarse-to-fine fashion. Extensive experiments on nuScenes and Waymo Open Dataset show that our proposed framework outperforms all state-of-the-art LiDAR-based and RGB+LiDAR-based methods with remarkable margins by using only point clouds as input. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_BE-STI_Spatial-Temporal_Integrated_Network_for_Class-Agnostic_Motion_Prediction_With_Bidirectional_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_BE-STI_Spatial-Temporal_Integrated_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_BE-STI_Spatial-Temporal_Integrated_Network_for_Class-Agnostic_Motion_Prediction_With_Bidirectional_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_BE-STI_Spatial-Temporal_Integrated_Network_for_Class-Agnostic_Motion_Prediction_With_Bidirectional_CVPR_2022_paper.html | CVPR 2022 | null |
A Structured Dictionary Perspective on Implicit Neural Representations | Gizem Yüce, Guillermo Ortiz-Jiménez, Beril Besbinar, Pascal Frossard | Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a novel unified perspective to theoretically analyse INRs. Leveraging results from harmonic analysis and deep learning theory, we show that most INR families are analogous to structured signal dictionaries whose atoms are integer harmonics of the set of initial mapping frequencies. This structure allows INRs to express signals with an exponentially increasing frequency support using a number of parameters that only grows linearly with depth. We also explore the inductive bias of INRs exploiting recent results about the empirical neural tangent kernel (NTK). Specifically, we show that the eigenfunctions of the NTK can be seen as dictionary atoms whose inner product with the target signal determines the final performance of their reconstruction. In this regard, we reveal that meta-learning has a reshaping effect on the NTK analogous to dictionary learning, building dictionary atoms as a combination of the examples seen during meta-training. Our results permit to design and tune novel INR architectures, but can also be of interest for the wider deep learning theory community. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuce_A_Structured_Dictionary_Perspective_on_Implicit_Neural_Representations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuce_A_Structured_Dictionary_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuce_A_Structured_Dictionary_Perspective_on_Implicit_Neural_Representations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuce_A_Structured_Dictionary_Perspective_on_Implicit_Neural_Representations_CVPR_2022_paper.html | CVPR 2022 | null |
Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization | Hao Jiang, Calvin Murdock, Vamsi Krishna Ithapu | Augmented reality devices have the potential to enhance human perception and enable other assistive functionalities in complex conversational environments. Effectively capturing the audio-visual context necessary for understanding these social interactions first requires detecting and localizing the voice activities of the device wearer and the surrounding people. These tasks are challenging due to their egocentric nature: the wearer's head motion may cause motion blur, surrounding people may appear in difficult viewing angles, and there may be occlusions, visual clutter, audio noise, and bad lighting. Under these conditions, previous state-of-the-art active speaker detection methods do not give satisfactory results. Instead, we tackle the problem from a new setting using both video and multi-channel microphone array audio. We propose a novel end-to-end deep learning approach that is able to give robust voice activity detection and localization results. In contrast to previous methods, our method localizes active speakers from all possible directions on the sphere, even outside the camera's field of view, while simultaneously detecting the device wearer's own voice activity. Our experiments show that the proposed method gives superior results, can run in real time, and is robust against noise and clutter. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Egocentric_Deep_Multi-Channel_Audio-Visual_Active_Speaker_Localization_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.01928 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Egocentric_Deep_Multi-Channel_Audio-Visual_Active_Speaker_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Egocentric_Deep_Multi-Channel_Audio-Visual_Active_Speaker_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Vision-Language Pre-Training With Triple Contrastive Learning | Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liqun Chen, Belinda Zeng, Trishul Chilimbi, Junzhou Huang | Vision-language representation learning largely benefits from image-text alignment through contrastive losses (e.g., InfoNCE loss). The success of this alignment strategy is attributed to its capability in maximizing the mutual information (MI) between an image and its matched text. However, simply performing cross-modal alignment (CMA) ignores data potential within each modality, which may result in degraded representations. For instance, although CMA-based models are able to map image-text pairs close together in the embedding space, they fail to ensure that similar inputs from the same modality stay close by. This problem can get even worse when the pre-training data is noisy. In this paper, we propose triple contrastive learning (TCL) for vision-language pre-training by leveraging both cross-modal and intra-modal self-supervision. Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning. To take advantage of localized and structural information from image and text input, TCL further maximizes the average MI between local regions of image/text and their global summary. To the best of our knowledge, ours is the first work that takes into account local structure information for multi-modality representation learning. Experimental evaluations show that our approach is competitive and achieves the new state of the art on various common down-stream vision-language tasks such as image-text retrieval and visual question answering. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Vision-Language_Pre-Training_With_Triple_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Vision-Language_Pre-Training_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.10401 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Vision-Language_Pre-Training_With_Triple_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Vision-Language_Pre-Training_With_Triple_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Structure-Aware Flow Generation for Human Body Reshaping | Jianqiang Ren, Yuan Yao, Biwen Lei, Miaomiao Cui, Xuansong Xie | Body reshaping is an important procedure in portrait photo retouching. Due to the complicated structure and multifarious appearance of human bodies, existing methods either fall back on the 3D domain via body morphable model or resort to keypoint-based image deformation, leading to inefficiency and unsatisfied visual quality. In this paper, we address these limitations by formulating an end-to-end flow generation architecture under the guidance of body structural priors, including skeletons and Part Affinity Fields, and achieve unprecedentedly controllable performance under arbitrary poses and garments. A compositional attention mechanism is introduced for capturing both visual perceptual correlations and structural associations of the human body to reinforce the manipulation consistency among related parts. For a comprehensive evaluation, we construct the first large-scale body reshaping dataset, namely BR-5K, which contains 5,000 portrait photos as well as professionally retouched targets. Extensive experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of visual performance, controllability, and efficiency. The dataset is available at our website: https://github.com/JianqiangRen/FlowBasedBodyReshaping. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Structure-Aware_Flow_Generation_for_Human_Body_Reshaping_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_Structure-Aware_Flow_Generation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Structure-Aware_Flow_Generation_for_Human_Body_Reshaping_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Structure-Aware_Flow_Generation_for_Human_Body_Reshaping_CVPR_2022_paper.html | CVPR 2022 | null |
Practical Learned Lossless JPEG Recompression With Multi-Level Cross-Channel Entropy Model in the DCT Domain | Lina Guo, Xinjie Shi, Dailan He, Yuanyuan Wang, Rui Ma, Hongwei Qin, Yan Wang | JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems. However, most recent progress on image compression mainly focuses on uncompressed images while ignoring trillions of already-existing JPEG images. To compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, we propose a deep learning based JPEG recompression method that operates on DCT domain and propose a Multi-Level Cross-Channel Entropy Model to compress the most informative Y component. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this is the first learned compression method that losslessly transcodes JPEG images to more storage-saving bitstreams. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Practical_Learned_Lossless_JPEG_Recompression_With_Multi-Level_Cross-Channel_Entropy_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Practical_Learned_Lossless_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16357 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Practical_Learned_Lossless_JPEG_Recompression_With_Multi-Level_Cross-Channel_Entropy_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Practical_Learned_Lossless_JPEG_Recompression_With_Multi-Level_Cross-Channel_Entropy_Model_CVPR_2022_paper.html | CVPR 2022 | null |
Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-Time | Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Jingyi Yu, Lan Xu | Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly on modeling static objects captured under multi-view settings where real-time rendering can be achieved with smart data structures, e.g., PlenOctree. In this paper, we present a novel Fourier PlenOctree (FPO) technique to tackle efficient neural modeling and real-time rendering of dynamic scenes captured under the free-view video (FVV) setting. The key idea in our FPO is a novel combination of generalized NeRF, PlenOctree representation, volumetric fusion and Fourier transform. To accelerate FPO construction, we present a novel coarse-to-fine fusion scheme that leverages the generalizable NeRF technique to generate the tree via spatial blending. To tackle dynamic scenes, we tailor the implicit network to model the Fourier coefficients of time-varying density and color attributes. Finally, we construct the FPO and train the Fourier coefficients directly on the leaves of a union PlenOctree structure of the dynamic sequence. We show that the resulting FPO enables compact memory overload to handle dynamic objects and supports efficient fine-tuning. Extensive experiments show that the proposed method is 3000 times faster than the original NeRF and achieves over an order of magnitude acceleration over SOTA while preserving high visual quality for the free-viewpoint rendering of unseen dynamic scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Fourier_PlenOctrees_for_Dynamic_Radiance_Field_Rendering_in_Real-Time_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Fourier_PlenOctrees_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.08614 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Fourier_PlenOctrees_for_Dynamic_Radiance_Field_Rendering_in_Real-Time_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Fourier_PlenOctrees_for_Dynamic_Radiance_Field_Rendering_in_Real-Time_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Answer Questions in Dynamic Audio-Visual Scenarios | Guangyao Li, Yake Wei, Yapeng Tian, Chenliang Xu, Ji-Rong Wen, Di Hu | In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos. The problem requires comprehensive multimodal understanding and spatio-temporal reasoning over audio-visual scenes. To benchmark this task and facilitate our study, we introduce a large-scale MUSIC-AVQA dataset, which contains more than 45K question-answer pairs covering 33 different question templates spanning over different modalities and question types. We develop several baselines and introduce a spatio-temporal grounded audio-visual network for the AVQA problem. Our results demonstrate that AVQA benefits from multisensory perception and our model outperforms recent A-, V-, and AVQA approaches. We believe that our built dataset has the potential to serve as testbed for evaluating and promoting progress in audio-visual scene understanding and spatio-temporal reasoning. Code and dataset: http://gewu-lab.github.io/MUSIC-AVQA/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Learning_To_Answer_Questions_in_Dynamic_Audio-Visual_Scenarios_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Learning_To_Answer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14072 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_To_Answer_Questions_in_Dynamic_Audio-Visual_Scenarios_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_To_Answer_Questions_in_Dynamic_Audio-Visual_Scenarios_CVPR_2022_paper.html | CVPR 2022 | null |
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