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Leveraging Equivariant Features for Absolute Pose Regression | Mohamed Adel Musallam, Vincent Gaudillière, Miguel Ortiz del Castillo, Kassem Al Ismaeil, Djamila Aouada | While end-to-end approaches have achieved state-of-the-art performance in many perception tasks, they are not yet able to compete with 3D geometry-based methods in pose estimation. Moreover, absolute pose regression has been shown to be more related to image retrieval. As a result, we hypothesize that the statistical features learned by classical Convolutional Neural Networks do not carry enough geometric information to reliably solve this inherently geometric task. In this paper, we demonstrate how a translation and rotation equivariant Convolutional Neural Network directly induces representations of camera motions into the feature space. We then show that this geometric property allows for implicitly augmenting the training data under a whole group of image plane-preserving transformations. Therefore, we argue that directly learning equivariant features is preferable than learning data-intensive intermediate representations. Comprehensive experimental validation demonstrates that our lightweight model outperforms existing ones on standard datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Musallam_Leveraging_Equivariant_Features_for_Absolute_Pose_Regression_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Musallam_Leveraging_Equivariant_Features_for_Absolute_Pose_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Musallam_Leveraging_Equivariant_Features_for_Absolute_Pose_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
Synthetic Aperture Imaging With Events and Frames | Wei Liao, Xiang Zhang, Lei Yu, Shijie Lin, Wen Yang, Ning Qiao | The Event-based Synthetic Aperture Imaging (E-SAI) has recently been proposed to see through extremely dense occlusions. However, the performance of E-SAI is not consistent under sparse occlusions due to the dramatic decrease of signal events. This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions. In particular, we first extract the feature from events and frames via multi-modal feature encoders and then apply a multi-stage fusion network for cross-modal enhancement and density-aware feature selection. Finally, a CNN decoder is employed to generate occlusion-free visual images from selected features. Extensive experiments show that our method effectively tackles varying densities of occlusions and achieves superior performance to the state-of-the-art SAI methods. Codes and datasets are available at https://github.com/smjsc/EF-SAI | https://openaccess.thecvf.com/content/CVPR2022/papers/Liao_Synthetic_Aperture_Imaging_With_Events_and_Frames_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liao_Synthetic_Aperture_Imaging_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Synthetic_Aperture_Imaging_With_Events_and_Frames_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Synthetic_Aperture_Imaging_With_Events_and_Frames_CVPR_2022_paper.html | CVPR 2022 | null |
CLIP-Event: Connecting Text and Images With Event Structures | Manling Li, Ruochen Xu, Shuohang Wang, Luowei Zhou, Xudong Lin, Chenguang Zhu, Michael Zeng, Heng Ji, Shih-Fu Chang | Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of events and their argument structures. In this work, we propose a contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (participant) roles. To achieve this, we take advantage of text information extraction technologies to obtain event structural knowledge, and utilize multiple prompt functions to contrast difficult negative descriptions by manipulating event structures. We also design an event graph alignment loss based on optimal transport to capture event argument structures. In addition, we collect a large event-rich dataset (106,875 images) for pretraining, which provides a more challenging image retrieval benchmark to assess the understanding of complicated lengthy sentences. Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction on Multimedia Event Extraction, achieving more than 5% absolute F-score gain in event extraction, as well as significant improvements on a variety of downstream tasks under zero-shot settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_CLIP-Event_Connecting_Text_and_Images_With_Event_Structures_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_CLIP-Event_Connecting_Text_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_CLIP-Event_Connecting_Text_and_Images_With_Event_Structures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_CLIP-Event_Connecting_Text_and_Images_With_Event_Structures_CVPR_2022_paper.html | CVPR 2022 | null |
MonoGround: Detecting Monocular 3D Objects From the Ground | Zequn Qin, Xi Li | Monocular 3D object detection has attracted great attention for its advantages in simplicity and cost. Due to the ill-posed 2D to 3D mapping essence from the monocular imaging process, monocular 3D object detection suffers from inaccurate depth estimation and thus has poor 3D detection results. To alleviate this problem, we propose to introduce the ground plane as a prior in the monocular 3d object detection. The ground plane prior serves as an additional geometric condition to the ill-posed mapping and an extra source in depth estimation. In this way, we can get a more accurate depth estimation from the ground. Meanwhile, to take full advantage of the ground plane prior, we propose a depth-align training strategy and a precise two-stage depth inference method tailored for the ground plane prior. It is worth noting that the introduced ground plane prior requires no extra data sources like LiDAR, stereo images, and depth information. Extensive experiments on the KITTI benchmark show that our method could achieve state-of-the-art results compared with other methods while maintaining a very fast speed. Our code, models, and training logs are available at https://github.com/cfzd/MonoGround. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qin_MonoGround_Detecting_Monocular_3D_Objects_From_the_Ground_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_MonoGround_Detecting_Monocular_3D_Objects_From_the_Ground_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_MonoGround_Detecting_Monocular_3D_Objects_From_the_Ground_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Visual Geo-Localization Benchmark | Gabriele Berton, Riccardo Mereu, Gabriele Trivigno, Carlo Masone, Gabriela Csurka, Torsten Sattler, Barbara Caputo | In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how different components and design choices in a VG pipeline impact the final results, both in terms of performance (recall@N metric) and system requirements (such as execution time and memory consumption); ii) establish a systematic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing backbone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data augmentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in extraction time and dataset storage requirement. Code and trained models are available at https://deep-vg-bench.herokuapp.com/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Berton_Deep_Visual_Geo-Localization_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Berton_Deep_Visual_Geo-Localization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03444 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Berton_Deep_Visual_Geo-Localization_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Berton_Deep_Visual_Geo-Localization_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Scaling Up Vision-Language Pre-Training for Image Captioning | Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, Lijuan Wang | In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art VinVL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million image-text pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail visual concepts when used in a zero-shot manner. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Scaling_Up_Vision-Language_Pre-Training_for_Image_Captioning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.12233 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Scaling_Up_Vision-Language_Pre-Training_for_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Scaling_Up_Vision-Language_Pre-Training_for_Image_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning | Yuan-Fu Yang, Min Sun | With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the development of new technology will bring many defect wafers. If these defect wafers have not been correctly inspected, the ineffective semiconductor processing on these defect wafers will cause additional impact to our environment, such as excessive carbon dioxide emission and energy consumption. In this paper, we utilize the information processing advantages of quantum computing to promote the defect learning defect review (DLDR). We propose a classical-quantum hybrid algorithm for deep learning on near-term quantum processors. By tuning parameters implemented on it, quantum circuit driven by our framework learns a given DLDR task, include of wafer defect map classification, defect pattern classification, and hotspot detection. In addition, we explore parametrized quantum circuits with different expressibility and entangling capacities. These results can be used to build a future roadmap to develop circuit-based quantum deep learning for semiconductor defect detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Semiconductor_Defect_Detection_by_Hybrid_Classical-Quantum_Deep_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Semiconductor_Defect_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Semiconductor_Defect_Detection_by_Hybrid_Classical-Quantum_Deep_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Semiconductor_Defect_Detection_by_Hybrid_Classical-Quantum_Deep_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
StyleGAN-V: A Continuous Video Generator With the Price, Image Quality and Perks of StyleGAN2 | Ivan Skorokhodov, Sergey Tulyakov, Mohamed Elhoseiny | Videos show continuous events, yet most -- if not all -- video synthesis frameworks treat them discretely in time. In this work, we think of videos of what they should be -- time-continuous signals, and extend the paradigm of neural representations to build a continuous-time video generator. For this, we first design continuous motion representations through the lens of positional embeddings. Then, we explore the question of training on very sparse videos and demonstrate that a good generator can be learned by using as few as 2 frames per clip. After that, we rethink the traditional image + video discriminators pair and design a holistic discriminator that aggregates temporal information by simply concatenating frames' features. This decreases the training cost and provides richer learning signal to the generator, making it possible to train directly on 1024x1024 videos for the first time. We build our model on top of StyleGAN2 and it is just 5% more expensive to train at the same resolution while achieving almost the same image quality. Moreover, our latent space features similar properties, enabling spatial manipulations that our method can propagate in time. We can generate arbitrarily long videos at arbitrary high frame rate, while prior work struggles to generate even 64 frames at a fixed rate. Our model is tested on four modern 256x256 and one 1024x1024-resolution video synthesis benchmarks. In terms of sheer metrics, it performs on average 30% better than the closest runner-up. Project website: https://universome.github.io/stylegan-v. | https://openaccess.thecvf.com/content/CVPR2022/papers/Skorokhodov_StyleGAN-V_A_Continuous_Video_Generator_With_the_Price_Image_Quality_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Skorokhodov_StyleGAN-V_A_Continuous_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Skorokhodov_StyleGAN-V_A_Continuous_Video_Generator_With_the_Price_Image_Quality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Skorokhodov_StyleGAN-V_A_Continuous_Video_Generator_With_the_Price_Image_Quality_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks | Xiangyu Qi, Tinghao Xie, Ruizhe Pan, Jifeng Zhu, Yong Yang, Kai Bu | One major goal of the AI security community is to securely and reliably produce and deploy deep learning models for real-world applications. To this end, data poisoning based backdoor attacks on deep neural networks (DNNs) in the production stage (or training stage) and corresponding defenses are extensively explored in recent years. Ironically, backdoor attacks in the deployment stage, which can often happen in unprofessional users' devices and are thus arguably far more threatening in real-world scenarios, draw much less attention of the community. We attribute this imbalance of vigilance to the weak practicality of existing deployment-stage backdoor attack algorithms and the insufficiency of real-world attack demonstrations. To fill the blank, in this work, we study the realistic threat of deployment-stage backdoor attacks on DNNs. We base our study on a commonly used deployment-stage attack paradigm --- adversarial weight attack, where adversaries selectively modify model weights to embed backdoor into deployed DNNs. To approach realistic practicality, we propose the first gray-box and physically realizable weights attack algorithm for backdoor injection, namely subnet replacement attack (SRA), which only requires architecture information of the victim model and can support physical triggers in the real world. Extensive experimental simulations and system-level real-world attack demonstrations are conducted. Our results not only suggest the effectiveness and practicality of the proposed attack algorithm, but also reveal the practical risk of a novel type of computer virus that may widely spread and stealthily inject backdoor into DNN models in user devices. By our study, we call for more attention to the vulnerability of DNNs in the deployment stage. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qi_Towards_Practical_Deployment-Stage_Backdoor_Attack_on_Deep_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qi_Towards_Practical_Deployment-Stage_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qi_Towards_Practical_Deployment-Stage_Backdoor_Attack_on_Deep_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qi_Towards_Practical_Deployment-Stage_Backdoor_Attack_on_Deep_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Scaling Vision Transformers | Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer | Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhai_Scaling_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhai_Scaling_Vision_Transformers_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.04560 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhai_Scaling_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhai_Scaling_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering | Sateesh Kumar, Sanjay Haresh, Awais Ahmed, Andrey Konin, M. Zeeshan Zia, Quoc-Huy Tran | We present a novel approach for unsupervised activity segmentation which uses video frame clustering as a pretext task and simultaneously performs representation learning and online clustering. This is in contrast with prior works where representation learning and clustering are often performed sequentially. We leverage temporal information in videos by employing temporal optimal transport. In particular, we incorporate a temporal regularization term which preserves the temporal order of the activity into the standard optimal transport module for computing pseudo-label cluster assignments. The temporal optimal transport module enables our approach to learn effective representations for unsupervised activity segmentation. Furthermore, previous methods require storing learned features for the entire dataset before clustering them in an offline manner, whereas our approach processes one mini-batch at a time in an online manner. Extensive evaluations on three public datasets, i.e. 50-Salads, YouTube Instructions, and Breakfast, and our dataset, i.e., Desktop Assembly, show that our approach performs on par with or better than previous methods, despite having significantly less memory constraints. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kumar_Unsupervised_Action_Segmentation_by_Joint_Representation_Learning_and_Online_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kumar_Unsupervised_Action_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.13353 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kumar_Unsupervised_Action_Segmentation_by_Joint_Representation_Learning_and_Online_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kumar_Unsupervised_Action_Segmentation_by_Joint_Representation_Learning_and_Online_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
Pin the Memory: Learning To Generalize Semantic Segmentation | Jin Kim, Jiyoung Lee, Jungin Park, Dongbo Min, Kwanghoon Sohn | The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new challenging domains, that is directly concerned with the generalization capability of the model. In this paper, we present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Especially, our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains. Upon the meta-learning concept, we repeatedly train memory-guided networks and simulate virtual test to 1) learn how to memorize a domain-agnostic and distinct information of classes and 2) offer an externally settled memory as a class-guidance to reduce the ambiguity of representation in the test data of arbitrary unseen domain. To this end, we also propose memory divergence and feature cohesion losses, which encourage to learn memory reading and update processes for category-aware domain generalization. Extensive experiments for semantic segmentation demonstrate the superior generalization capability of our method over state-of-the-art works on various benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Pin_the_Memory_Learning_To_Generalize_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Pin_the_Memory_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Pin_the_Memory_Learning_To_Generalize_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Pin_the_Memory_Learning_To_Generalize_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
LISA: Learning Implicit Shape and Appearance of Hands | Enric Corona, Tomas Hodan, Minh Vo, Francesc Moreno-Noguer, Chris Sweeney, Richard Newcombe, Lingni Ma | This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide dense surface correspondences, be reconstructed from images in the wild and easily animated. We train LISA by minimizing the shape and appearance losses on a large set of multi-view RGB image sequences annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand local coordinate, our model predicts the color and the signed distance with respect to each hand bone independently, and then combines the per-bone predictions using predicted skinning weights. The shape, color and pose representations are disentangled by design, allowing to estimate or animate only selected parameters. We experimentally demonstrate that LISA can accurately reconstruct a dynamic hand from monocular or multi-view sequences, achieving a noticeably higher quality of reconstructed hand shapes compared to baseline approaches. Project page: https://www.iri.upc.edu/people/ecorona/lisa/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Corona_LISA_Learning_Implicit_Shape_and_Appearance_of_Hands_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Corona_LISA_Learning_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01695 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Corona_LISA_Learning_Implicit_Shape_and_Appearance_of_Hands_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Corona_LISA_Learning_Implicit_Shape_and_Appearance_of_Hands_CVPR_2022_paper.html | CVPR 2022 | null |
DiGS: Divergence Guided Shape Implicit Neural Representation for Unoriented Point Clouds | Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Stephen Gould | Shape implicit neural representations (INR) have recently shown to be effective in shape analysis and reconstruction tasks. Existing INRs require point coordinates to learn the implicit level sets of the shape. When a normal vector is available for each point, a higher fidelity representation can be learned, however normal vectors are often not provided as raw data. Furthermore, the method's initialization has been shown to play a crucial role for surface reconstruction. In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input. We show that incorporating a soft constraint on the divergence of the distance function favours smooth solutions that reliably orients gradients to match the unknown normal at each point, in some cases even better than approaches that use ground truth normal vectors directly. Additionally, we introduce a novel geometric initialization method for sinusoidal INRs that further improves convergence to the desired solution. We evaluate the effectiveness of our approach on the task of surface reconstruction and shape space learning and show SOTA performance compared to other unoriented methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ben-Shabat_DiGS_Divergence_Guided_Shape_Implicit_Neural_Representation_for_Unoriented_Point_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ben-Shabat_DiGS_Divergence_Guided_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ben-Shabat_DiGS_Divergence_Guided_Shape_Implicit_Neural_Representation_for_Unoriented_Point_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ben-Shabat_DiGS_Divergence_Guided_Shape_Implicit_Neural_Representation_for_Unoriented_Point_CVPR_2022_paper.html | CVPR 2022 | null |
Iterative Deep Homography Estimation | Si-Yuan Cao, Jianxin Hu, Zehua Sheng, Hui-Liang Shen | We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator, the iterator of IHN has tied weights and is completely trainable. IHN achieves state-of-the-art accuracy on several datasets including challenging scenes. We propose 2 versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with moving objects. Both versions can be arranged in 1-scale for efficiency or 2-scale for accuracy. We show that the basic 1-scale IHN already outperforms most of the existing methods. On a variety of datasets, the 2-scale IHN outperforms all competitors by a large gap. We introduce IHN-mov by producing an inlier mask to further improve the estimation accuracy of moving-objects scenes. We experimentally show that the iterative framework of IHN can achieve 95% error reduction while considerably saving network parameters. When processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8x the speed of IC-LK iterator. Source code is available at https://github.com/imdumpl78/IHN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_Iterative_Deep_Homography_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_Iterative_Deep_Homography_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15982 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Iterative_Deep_Homography_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Iterative_Deep_Homography_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Learning of Semantic Correspondence With Pseudo-Labels | Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, Seungryong Kim | Establishing dense correspondences across semantically similar images remains a challenging task due to the significant intra-class variations and background clutters. Traditionally, a supervised loss was used for training the matching networks, which requires tremendous manually-labeled data, while some methods suggested a self-supervised or weakly-supervised loss to mitigate the reliance on the labeled data, but with limited performance. In this paper, we present a simple, but effective solution for semantic correspondence, called SemiMatch, that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels. Specifically, our framework generates the pseudo-labels using the model's prediction itself between source and weakly-augmented target, and uses pseudo-labels to learn the model again between source and strongly-augmented target, which improves the robustness of the model. We also present a novel confidence measure for pseudo-labels and data augmentation tailored for semantic correspondence. In experiments, SemiMatch achieves state-of-the-art performance on various benchmarks by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Semi-Supervised_Learning_of_Semantic_Correspondence_With_Pseudo-Labels_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Semi-Supervised_Learning_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16038 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Semi-Supervised_Learning_of_Semantic_Correspondence_With_Pseudo-Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Semi-Supervised_Learning_of_Semantic_Correspondence_With_Pseudo-Labels_CVPR_2022_paper.html | CVPR 2022 | null |
Learned Queries for Efficient Local Attention | Moab Arar, Ariel Shamir, Amit H. Bermano | Vision Transformers (ViT) serve as powerful vision models. Unlike convolutional neural networks, which dominated vision research in previous years, vision transformers enjoy the ability to capture long-range dependencies in the data. Nonetheless, an integral part of any transformer architecture, the self-attention mechanism, suffers from high latency and inefficient memory utilization, making it less suitable for high-resolution input images. To alleviate these shortcomings, hierarchical vision models locally employ self-attention on non-interleaving windows. This relaxation reduces the complexity to be linear in the input size; however, it limits the cross-window interaction, hurting the model performance. In this paper, we propose a new shift-invariant local attention layer, called query and attend (QnA), that aggregates the input locally in an overlapping manner, much like convolutions. The key idea behind QnA is to introduce learned queries, which allow fast and efficient implementation. We verify the effectiveness of our layer by incorporating it into a hierarchical vision transformer model. We show improvements in speed and memory complexity while achieving comparable accuracy with state-of-the-art models. Finally, our layer scales especially well with window size, requiring up to x10 less memory while being up to x5 faster than existing methods. The code is publicly available at https://github.com/moabarar/qna. | https://openaccess.thecvf.com/content/CVPR2022/papers/Arar_Learned_Queries_for_Efficient_Local_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Arar_Learned_Queries_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11435 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Arar_Learned_Queries_for_Efficient_Local_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Arar_Learned_Queries_for_Efficient_Local_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Stereoscopic Universal Perturbations Across Different Architectures and Datasets | Zachary Berger, Parth Agrawal, Tian Yu Liu, Stefano Soatto, Alex Wong | We study the effect of adversarial perturbations of images on deep stereo matching networks for the disparity estimation task. We present a method to craft a single set of perturbations that, when added to any stereo image pair in a dataset, can fool a stereo network to significantly alter the perceived scene geometry. Our perturbation images are "universal" in that they not only corrupt estimates of the network on the dataset they are optimized for, but also generalize to different architectures trained on different datasets. We evaluate our approach on multiple benchmark datasets where our perturbations can increase the D1-error (akin to fooling rate) of state-of-the-art stereo networks from 1% to as much as 87%. We investigate the effect of perturbations on the estimated scene geometry and identify object classes that are most vulnerable. Our analysis on the activations of registered points between left and right images led us to find architectural components that can increase robustness against adversaries. By simply designing networks with such components, one can reduce the effect of adversaries by up to 60.5%, which rivals the robustness of networks fine-tuned with costly adversarial data augmentation. Our design principle also improves their robustness against common image corruptions by an average of 70%. | https://openaccess.thecvf.com/content/CVPR2022/papers/Berger_Stereoscopic_Universal_Perturbations_Across_Different_Architectures_and_Datasets_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Berger_Stereoscopic_Universal_Perturbations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.06116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Berger_Stereoscopic_Universal_Perturbations_Across_Different_Architectures_and_Datasets_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Berger_Stereoscopic_Universal_Perturbations_Across_Different_Architectures_and_Datasets_CVPR_2022_paper.html | CVPR 2022 | null |
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars | Le Yang, Junwei Han, Dingwen Zhang | Online action detection has attracted increasing research interests in recent years. Current works model historical dependencies and anticipate the future to perceive the action evolution within a video segment and improve the detection accuracy. However, the existing paradigm ignores category-level modeling and does not pay sufficient attention to efficiency. Considering a category, its representative frames exhibit various characteristics. Thus, the category-level modeling can provide complimentary guidance to the temporal dependencies modeling. This paper develops an effective exemplar-consultation mechanism that first measures the similarity between a frame and exemplary frames, and then aggregates exemplary features based on the similarity weights. This is also an efficient mechanism, as both similarity measurement and feature aggregation require limited computations. Based on the exemplar-consultation mechanism, the long-term dependencies can be captured by regarding historical frames as exemplars, while the category-level modeling can be achieved by regarding representative frames from a category as exemplars. Due to the complementarity from the category-level modeling, our method employs a lightweight architecture but achieves new high performance on three benchmarks. In addition, using a spatio-temporal network to tackle video frames, our method makes a good trade-off between effectiveness and efficiency. Code is available at https://github.com/VividLe/Online-Action-Detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Colar_Effective_and_Efficient_Online_Action_Detection_by_Consulting_Exemplars_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01057 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Colar_Effective_and_Efficient_Online_Action_Detection_by_Consulting_Exemplars_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Colar_Effective_and_Efficient_Online_Action_Detection_by_Consulting_Exemplars_CVPR_2022_paper.html | CVPR 2022 | null |
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation | Xueyi Liu, Xiaomeng Xu, Anyi Rao, Chuang Gan, Li Yi | Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specific solutions by translating human understanding of the task to machine's learning process, which faces the risk of missing the optimal strategy since machines do not necessarily understand in the exact human way. Others try to use conventional task-agnostic approaches designed for domain generalization problems with no task prior knowledge considered. To solve the above issues, we propose AutoGPart, a generic method enabling training generalizable 3D part segmentation networks with the task prior considered. AutoGPart builds a supervision space with geometric prior knowledge encoded, and lets the machine to search for the optimal supervisions from the space for a specific segmentation task automatically. Extensive experiments on three generalizable 3D part segmentation tasks are conducted to demonstrate the effectiveness and versatility of AutoGPart. We demonstrate that the performance of segmentation networks using simple backbones can be significantly improved when trained with supervisions searched by our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_AutoGPart_Intermediate_Supervision_Search_for_Generalizable_3D_Part_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_AutoGPart_Intermediate_Supervision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_AutoGPart_Intermediate_Supervision_Search_for_Generalizable_3D_Part_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_AutoGPart_Intermediate_Supervision_Search_for_Generalizable_3D_Part_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos | Mathias Parger, Chengcheng Tang, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger | Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a video typically change little. By skipping identical image regions and truncating insignificant pixel updates, computational redundancy can in theory be reduced significantly. However, these theoretical savings have been difficult to translate into practice, as sparse updates hamper computational consistency and memory access coherence; which are key for efficiency on real hardware. With DeltaCNN, we present a sparse convolutional neural network framework that enables sparse frame-by-frame updates to accelerate video inference in practice. We provide sparse implementations for all typical CNN layers and propagate sparse feature updates end-to-end - without accumulating errors over time. DeltaCNN is applicable to all convolutional neural networks without retraining. To the best of our knowledge, we are the first to significantly outperform the dense reference, cuDNN, in practical settings, achieving speedups of up to 7x with only marginal differences in accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Parger_DeltaCNN_End-to-End_CNN_Inference_of_Sparse_Frame_Differences_in_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Parger_DeltaCNN_End-to-End_CNN_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03996 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parger_DeltaCNN_End-to-End_CNN_Inference_of_Sparse_Frame_Differences_in_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parger_DeltaCNN_End-to-End_CNN_Inference_of_Sparse_Frame_Differences_in_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging | Yisi Luo, Xi-Le Zhao, Deyu Meng, Tai-Xiang Jiang | Inverse problems in multi-dimensional imaging, e.g., completion, denoising, and compressive sensing, are challenging owing to the big volume of the data and the inherent ill-posedness. To tackle these issues, this work unsupervisedly learns a hierarchical low-rank tensor factorization (HLRTF) by solely using an observed multi-dimensional image. Specifically, we embed a deep neural network (DNN) into the tensor singular value decomposition framework and develop the HLRTF, which captures the underlying low-rank structures of multi-dimensional images with compact representation abilities. This DNN herein serves as a nonlinear transform from a vector to another to help obtain a better low-rank representation. Our HLRTF infers the parameters of the DNN and the underlying low-rank structure of the original data from its observation via the gradient descent using a non-reference loss function in an unsupervised manner. To address the vanishing gradient in extreme scenarios, e.g., structural missing pixels, we introduce a parametric total variation regularization to constrain the DNN parameters and the tensor factor parameters with theoretical analysis. We apply our HLRTF for typical inverse problems in multi-dimensional imaging including completion, denoising, and snapshot spectral imaging, which demonstrates its generality and wide applicability. Extensive results illustrate the superiority of our method as compared with state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_HLRTF_Hierarchical_Low-Rank_Tensor_Factorization_for_Inverse_Problems_in_Multi-Dimensional_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_HLRTF_Hierarchical_Low-Rank_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_HLRTF_Hierarchical_Low-Rank_Tensor_Factorization_for_Inverse_Problems_in_Multi-Dimensional_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_HLRTF_Hierarchical_Low-Rank_Tensor_Factorization_for_Inverse_Problems_in_Multi-Dimensional_CVPR_2022_paper.html | CVPR 2022 | null |
Leveraging Self-Supervision for Cross-Domain Crowd Counting | Weizhe Liu, Nikita Durasov, Pascal Fua | State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from being deployed in emergencies during which data annotation is either too costly or cannot be obtained fast enough. One popular solution is to use synthetic data for training. Unfortunately, due to domain shift, the resulting models generalize poorly on real imagery. We remedy this shortcoming by training with both synthetic images, along with their associated labels, and unlabeled real images. To this end, we force our network to learn perspective-aware features by training it to recognize upside-down real images from regular ones and incorporate into it the ability to predict its own uncertainty so that it can generate useful pseudo labels for fine-tuning purposes. This yields an algorithm that consistently outperforms state-of-the-art cross-domain crowd counting ones without any extra computation at inference time. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Leveraging_Self-Supervision_for_Cross-Domain_Crowd_Counting_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2103.16291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Leveraging_Self-Supervision_for_Cross-Domain_Crowd_Counting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Leveraging_Self-Supervision_for_Cross-Domain_Crowd_Counting_CVPR_2022_paper.html | CVPR 2022 | null |
MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution | Wuyuan Xie, Tengcong Huang, Miaohui Wang | With the rapid development of display technology, it has become an urgent need to obtain realistic 3D surfaces with as high-quality as possible. Due to the unstructured and irregular nature of 3D object data, it is usually difficult to obtain high-quality surface details and geometry textures at a low cost. In this article, we propose an effective multimodal-driven deep neural network to perform 3D surface super-resolution in 2D normal domain, which is simple, accurate, and robust to the above difficulty. To leverage the multimodal information from different perspectives, we jointly consider the texture, depth, and normal modalities to simultaneously restore fine-grained surface details as well as preserve geometry structures. To better utilize the cross-modality information, we explore a two-bridge normal method with a transformer structure for feature alignment, and investigate an affine transform module for fusing multimodal features. Extensive experimental results on public and our newly constructed photometric stereo dataset demonstrate that the proposed method delivers promising surface geometry details compared with nine competitive schemes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_MNSRNet_Multimodal_Transformer_Network_for_3D_Surface_Super-Resolution_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_MNSRNet_Multimodal_Transformer_Network_for_3D_Surface_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_MNSRNet_Multimodal_Transformer_Network_for_3D_Surface_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders | Minyoung Kim | Variational autoencoder (VAE) is a very successful generative model whose key element is the so called amortized inference network, which can perform test time inference using a single feed forward pass. Unfortunately, this comes at the cost of degraded accuracy in posterior approximation, often underperforming the instance-wise variational optimization. Although the latest semi-amortized approaches mitigate the issue by performing a few variational optimization updates starting from the VAE's amortized inference output, they inherently suffer from computational overhead for inference at test time. In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of the variational posterior as random Gaussian processes (GP). The motivation is that the deviation of the VAE's amortized posterior distribution from the true posterior can be regarded as random noise, which allows us to view the approximation error as uncertainty in posterior approximation that can be dealt with in a principled GP manner. In particular, our model can quantify the difficulty in posterior approximation by a Gaussian variational density. Inference in our GP model is done by a single feed forward pass through the network, significantly faster than semi-amortized methods. We show that our approach attains higher test data likelihood than the state-of-the-arts on several benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Gaussian_Process_Modeling_of_Approximate_Inference_Errors_for_Variational_Autoencoders_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Gaussian_Process_Modeling_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Gaussian_Process_Modeling_of_Approximate_Inference_Errors_for_Variational_Autoencoders_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Gaussian_Process_Modeling_of_Approximate_Inference_Errors_for_Variational_Autoencoders_CVPR_2022_paper.html | CVPR 2022 | null |
PlaneMVS: 3D Plane Reconstruction From Multi-View Stereo | Jiachen Liu, Pan Ji, Nitin Bansal, Changjiang Cai, Qingan Yan, Xiaolei Huang, Yi Xu | We present a novel framework named PlaneMVS for 3D plane reconstruction from multiple input views with known camera poses. Most previous learning-based plane reconstruction methods reconstruct 3D planes from single images, which highly rely on single-view regression and suffer from depth scale ambiguity. In contrast, we reconstruct 3D planes with a multi-view-stereo (MVS) pipeline that takes advantage of multi-view geometry. We decouple plane reconstruction into a semantic plane detection branch and a plane MVS branch. The semantic plane detection branch is based on a single-view plane detection framework but with differences. The plane MVS branch adopts a set of slanted plane hypotheses to replace conventional depth hypotheses to perform plane sweeping strategy and finally learns pixel-level plane parameters and its planar depth map. We present how the two branches are learned in a balanced way, and propose a soft-pooling loss to associate the outputs of the two branches and make them benefit from each other. Extensive experiments on various indoor datasets show that PlaneMVS significantly outperforms state-of-the-art (SOTA) single-view plane reconstruction methods on both plane detection and 3D geometry metrics. Our method even outperforms a set of SOTA learning-based MVS methods thanks to the learned plane priors. To the best of our knowledge, this is the first work on 3D plane reconstruction within an end-to-end MVS framework. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_PlaneMVS_3D_Plane_Reconstruction_From_Multi-View_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_PlaneMVS_3D_Plane_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_PlaneMVS_3D_Plane_Reconstruction_From_Multi-View_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_PlaneMVS_3D_Plane_Reconstruction_From_Multi-View_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
Scene Graph Expansion for Semantics-Guided Image Outpainting | Chiao-An Yang, Cheng-Yo Tan, Wan-Cyuan Fan, Cheng-Fu Yang, Meng-Lin Wu, Yu-Chiang Frank Wang | In this paper, we address the task of semantics-guided image outpainting, which is to complete an image by generating semantically practical content. Different from most existing image outpainting works, we approach the above task by understanding and completing image semantics at the scene graph level. In particular, we propose a novel network of Scene Graph Transformer (SGT), which is designed to take node and edge features as inputs for modeling the associated structural information. To better understand and process graph-based inputs, our SGT uniquely performs feature attention at both node and edge levels. While the former views edges as relationship regularization, the latter observes the co-occurrence of nodes for guiding the attention process. We demonstrate that, given a partial input image with its layout and scene graph, our SGT can be applied for scene graph expansion and its conversion to a complete layout. Following state-of-the-art layout-to-image conversions works, the task of image outpainting can be completed with sufficient and practical semantics introduced. Extensive experiments are conducted on the datasets of MS-COCO and Visual Genome, which quantitatively and qualitatively confirm the effectiveness of our proposed SGT and outpainting frameworks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Scene_Graph_Expansion_for_Semantics-Guided_Image_Outpainting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Scene_Graph_Expansion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Scene_Graph_Expansion_for_Semantics-Guided_Image_Outpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Scene_Graph_Expansion_for_Semantics-Guided_Image_Outpainting_CVPR_2022_paper.html | CVPR 2022 | null |
SoftGroup for 3D Instance Segmentation on Point Clouds | Thang Vu, Kookhoi Kim, Tung M. Luu, Thanh Nguyen, Chang D. Yoo | Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of +6.2% on the ScanNet v2 hidden test set and +6.8% on S3DIS Area 5 in terms of AP50. SoftGroup is also fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset. The source code and trained models for both datasets are available at https://github.com/thangvubk/SoftGroup.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vu_SoftGroup_for_3D_Instance_Segmentation_on_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
SharpContour: A Contour-Based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation | Chenming Zhu, Xuanye Zhang, Yanran Li, Liangdong Qiu, Kai Han, Xiaoguang Han | Excellent performance has been achieved on instance segmentation but the quality on the boundary area remains unsatisfactory, which leads to a rising attention on boundary refinement. For practical use, an ideal post-processing refinement scheme are required to be accurate, generic and efficient. However, most of existing approaches propose pixel-wise refinement, which either introduce a massive computation cost or design specifically for different backbone models. Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas. In this paper, we propose an efficient contour-based boundary refinement approach, named SharpContour, to tackle the segmentation of boundary area. We design a novel contour evolution process together with an Instance-aware Point Classifier. Our method deforms the contour iteratively by updating offsets in a discrete manner. Differing from existing contour evolution methods, SharpContour estimates each offset more independently so that it predicts much sharper and accurate contours. Notably, our method is generic to seamlessly work with diverse existing models with a small computational cost. Experiments show that SharpContour achieves competitive gains whilst preserving high efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_SharpContour_A_Contour-Based_Boundary_Refinement_Approach_for_Efficient_and_Accurate_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_SharpContour_A_Contour-Based_Boundary_Refinement_Approach_for_Efficient_and_Accurate_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_SharpContour_A_Contour-Based_Boundary_Refinement_Approach_for_Efficient_and_Accurate_CVPR_2022_paper.html | CVPR 2022 | null |
MVS2D: Efficient Multi-View Stereo via Attention-Driven 2D Convolutions | Zhenpei Yang, Zhile Ren, Qi Shan, Qixing Huang | Deep learning has made significant impacts on multi-view stereo systems. State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution operations to recover the input image's pixel-wise depth. While such end-to-end learning of plane-sweeping stereo advances public benchmarks' accuracy, they are typically very slow to compute. We present MVS2D, a highly efficient multi-view stereo algorithm that seamlessly integrates multi-view constraints into single-view networks via an attention mechanism. Since MVS2Donly builds on 2D convolutions, it is at least 2x faster than all the notable counterparts. Moreover, our algorithm produces precise depth estimations and 3D reconstructions, achieving state-of-the-art results on challenging benchmarks ScanNet, SUN3D, RGBD, and the classical DTU dataset. our algorithm also out-performs all other algorithms in the setting of inexact camera poses. Our code is released at https://github.com/zhenpeiyang/MVS2D | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_MVS2D_Efficient_Multi-View_Stereo_via_Attention-Driven_2D_Convolutions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_MVS2D_Efficient_Multi-View_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.13325 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_MVS2D_Efficient_Multi-View_Stereo_via_Attention-Driven_2D_Convolutions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_MVS2D_Efficient_Multi-View_Stereo_via_Attention-Driven_2D_Convolutions_CVPR_2022_paper.html | CVPR 2022 | null |
FIBA: Frequency-Injection Based Backdoor Attack in Medical Image Analysis | Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, Dacheng Tao | In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e.g., X-Ray, CT, and MRI) and analysis tasks (e.g., classification, detection, and segmentation). Most existing BA methods are designed to attack natural image classification models, which apply spatial triggers to training images and inevitably corrupt the semantics of poisoned pixels, leading to the failures of attacking dense prediction models. To address this issue, we propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images. Since it preserves the semantics of the poisoned image pixels, FIBA can perform attacks on both classification and dense prediction models. Experiments on three benchmarks in MIA (i.e., ISIC-2019 for skin lesion classification, KiTS-19 for kidney tumor segmentation, and EAD-2019 for endoscopic artifact detection), validate the effectiveness of FIBA and its superiority over state-of-the-art methods in attacking MIA models as well as bypassing backdoor defense. The code will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Feng_FIBA_Frequency-Injection_Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01148 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement | Beomyoung Kim, YoungJoon Yoo, Chae Eun Rhee, Junmo Kim | Weakly-supervised instance segmentation (WSIS) has been considered as a more challenging task than weakly-supervised semantic segmentation (WSSS). Compared to WSSS, WSIS requires instance-wise localization, which is difficult to extract from image-level labels. To tackle the problem, most WSIS approaches use off-the-shelf proposal techniques that require pre-training with instance or object level labels, deviating the fundamental definition of the fully-image-level supervised setting. In this paper, we propose a novel approach including two innovative components. First, we propose a semantic knowledge transfer to obtain pseudo instance labels by transferring the knowledge of WSSS to WSIS while eliminating the need for the off-the-shelf proposals. Second, we propose a self-refinement method to refine the pseudo instance labels in a self-supervised scheme and to use the refined labels for training in an online manner. Here, we discover an erroneous phenomenon, semantic drift, that occurred by the missing instances in pseudo instance labels categorized as background class. This semantic drift occurs confusion between background and instance in training and consequently degrades the segmentation performance. We term this problem as semantic drift problem and show that our proposed self-refinement method eliminates the semantic drift problem. The extensive experiments on PASCAL VOC 2012 and MS COCO demonstrate the effectiveness of our approach, and we achieve a considerable performance without off-the-shelf proposal techniques. The code is available at https://github.com/clovaai/BESTIE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Beyond_Semantic_to_Instance_Segmentation_Weakly-Supervised_Instance_Segmentation_via_Semantic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Beyond_Semantic_to_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.09477 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Beyond_Semantic_to_Instance_Segmentation_Weakly-Supervised_Instance_Segmentation_via_Semantic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Beyond_Semantic_to_Instance_Segmentation_Weakly-Supervised_Instance_Segmentation_via_Semantic_CVPR_2022_paper.html | CVPR 2022 | null |
Bridged Transformer for Vision and Point Cloud 3D Object Detection | Yikai Wang, TengQi Ye, Lele Cao, Wenbing Huang, Fuchun Sun, Fengxiang He, Dacheng Tao | 3D object detection is a crucial research topic in computer vision, which usually uses 3D point clouds as input in conventional setups. Recently, there is a trend of leveraging multiple sources of input data, such as complementing the 3D point cloud with 2D images that often have richer color and fewer noises. However, due to the heterogeneous geometrics of the 2D and 3D representations, it prevents us from applying off-the-shelf neural networks to achieve multimodal fusion. To that end, we propose Bridged Transformer (BrT), an end-to-end architecture for 3D object detection. BrT is simple and effective, which learns to identify 3D and 2D object bounding boxes from both points and image patches. A key element of BrT lies in the utilization of object queries for bridging 3D and 2D spaces, which unifies different sources of data representations in Transformer. We adopt a form of feature aggregation realized by point-to-patch projections which further strengthen the interaction between images and points. Moreover, BrT works seamlessly for fusing the point cloud with multi-view images. We experimentally show that BrT surpasses state-of-the-art methods on SUN RGB-D and ScanNetV2 datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Bridged_Transformer_for_Vision_and_Point_Cloud_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Bridged_Transformer_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Bridged_Transformer_for_Vision_and_Point_Cloud_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Bridged_Transformer_for_Vision_and_Point_Cloud_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Constrained Least Squares for Blind Image Super-Resolution | Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, Shuaicheng Liu | In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules. Following the common practices of blind SR, our method proposes to improve both the kernel estimation as well as the kernel-based high-resolution image restoration. To be more specific, we first reformulate the degradation model such that the deblurring kernel estimation can be transferred into the low-resolution space. On top of this, we introduce a dynamic deep linear filter module. Instead of learning a fixed kernel for all images, it can adaptively generate deblurring kernel weights conditional on the input and yield a more robust kernel estimation. Subsequently, a deep constrained least square filtering module is applied to generate clean features based on the reformulation and estimated kernel. The deblurred feature and the low input image feature are then fed into a dual-path structured SR network and restore the final high-resolution result. To evaluate our method, we further conduct evaluations on several benchmarks, including Gaussian8 and DIV2KRK. Our experiments demonstrate that the proposed method achieves better accuracy and visual improvements against state-of-the-art methods. Codes and models are available at https://github.com/megvii-research/DCLS-SR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Deep_Constrained_Least_Squares_for_Blind_Image_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_Deep_Constrained_Least_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.07508 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Deep_Constrained_Least_Squares_for_Blind_Image_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Deep_Constrained_Least_Squares_for_Blind_Image_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
EDTER: Edge Detection With Transformer | Mengyang Pu, Yaping Huang, Yuming Liu, Qingji Guan, Haibin Ling | Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually suppressed with the enlarging of receptive fields. Recently, vision transformer has shown excellent capability in capturing long-range dependencies. Inspired by this, we propose a novel transformer-based edge detector, Edge Detection TransformER (EDTER), to extract clear and crisp object boundaries and meaningful edges by exploiting the full image context information and detailed local cues simultaneously. EDTER works in two stages. In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches. Then in Stage II, a local transformer encoder works on fine-grained patches to excavate the short-range local cues. Each transformer encoder is followed by an elaborately designed Bi-directional Multi-Level Aggregation decoder to achieve high-resolution features. Finally, the global context and local cues are combined by a Feature Fusion Module and fed into a decision head for edge prediction. Extensive experiments on BSDS500, NYUDv2, and Multicue demonstrate the superiority of EDTER in comparison with state-of-the-arts. The source code is available at https://github.com/MengyangPu/EDTER. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pu_EDTER_Edge_Detection_With_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pu_EDTER_Edge_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pu_EDTER_Edge_Detection_With_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pu_EDTER_Edge_Detection_With_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, Ling-Yu Duan | Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Fine-Tuning_Global_Model_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09249 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.html | CVPR 2022 | null |
JIFF: Jointly-Aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction | Yukang Cao, Guanying Chen, Kai Han, Wenqi Yang, Kwan-Yee K. Wong | This paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face details in their reconstructions. This largely degrades user experience in applications like 3D telepresence. In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach. We employ a 3D morphable face model as our shape prior and compute space-aligned 3D features that capture detailed face geometry information. Such space-aligned 3D features are combined with pixel-aligned 2D features to jointly predict an implicit face function for high quality face reconstruction. We further extend our pipeline and introduce a coarse-to-fine architecture to predict high quality texture for our detailed face model. Extensive evaluations have been carried out on public datasets and our proposed JIFF has demonstrated superior performance (both quantitatively and qualitatively) over existing state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_JIFF_Jointly-Aligned_Implicit_Face_Function_for_High_Quality_Single_View_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_JIFF_Jointly-Aligned_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10549 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_JIFF_Jointly-Aligned_Implicit_Face_Function_for_High_Quality_Single_View_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_JIFF_Jointly-Aligned_Implicit_Face_Function_for_High_Quality_Single_View_CVPR_2022_paper.html | CVPR 2022 | null |
Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them From 2D Renderings | Innfarn Yoo, Huiwen Chang, Xiyang Luo, Ondrej Stava, Ce Liu, Peyman Milanfar, Feng Yang | Digital watermarking is widely used for copyright protection. Traditional 3D watermarking approaches or commercial software are typically designed to embed messages into 3D meshes, and later retrieve the messages directly from distorted/undistorted watermarked 3D meshes. However, in many cases, users only have access to rendered 2D images instead of 3D meshes. Unfortunately, retrieving messages from 2D renderings of 3D meshes is still challenging and underexplored. We introduce a novel end-to-end learning framework to solve this problem through: 1) an encoder to covertly embed messages in both mesh geometry and textures; 2) a differentiable renderer to render watermarked 3D objects from different camera angles and under varied lighting conditions; 3) a decoder to recover the messages from 2D rendered images. From our experiments, we show that our model can learn to embed information visually imperceptible to humans, and to retrieve the embedded information from 2D renderings that undergo 3D distortions. In addition, we demonstrate that our method can also work with other renderers, such as ray tracers and real-time renderers with and without fine-tuning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yoo_Deep_3D-to-2D_Watermarking_Embedding_Messages_in_3D_Meshes_and_Extracting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yoo_Deep_3D-to-2D_Watermarking_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2104.13450 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yoo_Deep_3D-to-2D_Watermarking_Embedding_Messages_in_3D_Meshes_and_Extracting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yoo_Deep_3D-to-2D_Watermarking_Embedding_Messages_in_3D_Meshes_and_Extracting_CVPR_2022_paper.html | CVPR 2022 | null |
Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning | Chia-Wen Kuo, Zsolt Kira | Significant progress has been made on visual captioning, largely relying on pre-trained features and later fixed object detectors that serve as rich inputs to auto-regressive models. A key limitation of such methods, however, is that the output of the model is conditioned only on the object detector's outputs. The assumption that such outputs can represent all necessary information is unrealistic, especially when the detector is transferred across datasets. In this work, we reason about the graphical model induced by this assumption, and propose to add an auxiliary input to represent missing information such as object relationships. We specifically propose to mine attributes and relationships from the Visual Genome dataset and condition the captioning model on them. Crucially, we propose (and show to be important) the use of a multi-modal pre-trained model (CLIP) to retrieve such contextual descriptions. Further, the object detector outputs are fixed due to a frozen model and hence do not have sufficient richness to allow the captioning model to properly ground them. As a result, we propose to condition both the detector and description outputs on the image, and show qualitatively that this can improve grounding. We validate our method on image captioning, perform thorough analyses of each component and importance of the pre-trained multi-modal model, and demonstrate significant improvements over the current state of the art, specifically +7.5% in CIDEr and +1.3% in BLEU-4 metrics. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kuo_Beyond_a_Pre-Trained_Object_Detector_Cross-Modal_Textual_and_Visual_Context_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kuo_Beyond_a_Pre-Trained_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.04363 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kuo_Beyond_a_Pre-Trained_Object_Detector_Cross-Modal_Textual_and_Visual_Context_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kuo_Beyond_a_Pre-Trained_Object_Detector_Cross-Modal_Textual_and_Visual_Context_CVPR_2022_paper.html | CVPR 2022 | null |
Symmetry-Aware Neural Architecture for Embodied Visual Exploration | Shuang Liu, Takayuki Okatani | Visual exploration is a task that seeks to visit all the navigable areas of an environment as quickly as possible. The existing methods employ deep reinforcement learning (RL) as the standard tool for the task. However, they tend to be vulnerable to statistical shifts between the training and test data, resulting in poor generalization over novel environments that are out-of-distribution (OOD) from the training data. In this paper, we attempt to improve the generalization ability by utilizing the inductive biases available for the task. Employing the active neural SLAM (ANS) that learns exploration policies with the advantage actor-critic (A2C) method as the base framework, we first point out that the mappings represented by the actor and the critic should satisfy specific symmetries. We then propose a network design for the actor and the critic to inherently attain these symmetries. Specifically, we use G-convolution instead of the standard convolution and insert the semi-global polar pooling (SGPP) layer, which we newly design in this study, in the last section of the critic network. Experimental results show that our method increases area coverage by 8.1 square meters when trained on the Gibson dataset and tested on the Matterport3D dataset, establishing the new state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Symmetry-Aware_Neural_Architecture_for_Embodied_Visual_Exploration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Symmetry-Aware_Neural_Architecture_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Symmetry-Aware_Neural_Architecture_for_Embodied_Visual_Exploration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Symmetry-Aware_Neural_Architecture_for_Embodied_Visual_Exploration_CVPR_2022_paper.html | CVPR 2022 | null |
AirObject: A Temporally Evolving Graph Embedding for Object Identification | Nikhil Varma Keetha, Chen Wang, Yuheng Qiu, Kuan Xu, Sebastian Scherer | Object encoding and identification are vital for robotic tasks such as autonomous exploration, semantic scene understanding, and re-localization. Previous approaches have attempted to either track objects or generate descriptors for object identification. However, such systems are limited to a "fixed" partial object representation from a single viewpoint. In a robot exploration setup, there is a requirement for a temporally "evolving" global object representation built as the robot observes the object from multiple viewpoints. Furthermore, given the vast distribution of unknown novel objects in the real world, the object identification process must be class-agnostic. In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects. Specifically, the global 3D object embeddings are generated using a temporal convolutional network across structural information of multiple frames obtained from a graph attention-based encoding method. We demonstrate that AirObject achieves the state-of-the-art performance for video object identification and is robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform, outperforming the state-of-the-art single-frame and sequential descriptors. To the best of our knowledge, AirObject is one of the first temporal object encoding methods. Source code is available at https://github.com/Nik-V9/AirObject. | https://openaccess.thecvf.com/content/CVPR2022/papers/Keetha_AirObject_A_Temporally_Evolving_Graph_Embedding_for_Object_Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Keetha_AirObject_A_Temporally_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2111.15150 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Keetha_AirObject_A_Temporally_Evolving_Graph_Embedding_for_Object_Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Keetha_AirObject_A_Temporally_Evolving_Graph_Embedding_for_Object_Identification_CVPR_2022_paper.html | CVPR 2022 | null |
From Representation to Reasoning: Towards Both Evidence and Commonsense Reasoning for Video Question-Answering | Jiangtong Li, Li Niu, Liqing Zhang | Video understanding has achieved great success in representation learning, such as video caption, video object grounding, and video descriptive question-answer. However, current methods still struggle on video reasoning, including evidence reasoning and commonsense reasoning. To facilitate deeper video understanding towards video reasoning, we present the task of Causal-VidQA, which includes four types of questions ranging from scene description (description) to evidence reasoning (explanation) and commonsense reasoning (prediction and counterfactual). For commonsense reasoning, we set up a two-step solution by answering the question and providing a proper reason. Through extensive experiments on existing VideoQA methods, we find that the state-of-the-art methods are strong in descriptions but weak in reasoning. We hope that Causal-VidQA can guide the research of video understanding from representation learning to deeper reasoning. The dataset and related resources are available at https://github.com/bcmi/Causal-VidQA.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_From_Representation_to_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.14895 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.html | CVPR 2022 | null |
Semantic-Aware Domain Generalized Segmentation | Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, Wen Li | Deep models trained on source domain lack generalization when evaluated on unseen target domains with different data distributions. The problem becomes even more pronounced when we have no access to target domain samples for adaptation. In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data. Existing approaches to tackle this problem standardize data into a unified distribution. We argue that while such a standardization promotes global normalization, the resulting features are not discriminative enough to get clear segmentation boundaries. To enhance separation between categories while simultaneously promoting domain invariance, we propose a framework including two novel modules: Semantic-Aware Normalization (SAN) and Semantic-Aware Whitening (SAW). Specifically, SAN focuses on category-level center alignment between features from different image styles, while SAW enforces distributed alignment for the already center-aligned features. With the help of SAN and SAW, we encourage both intraclass compactness and inter-class separability. We validate our approach through extensive experiments on widely-used datasets (i.e. GTAV, SYNTHIA, Cityscapes, Mapillary and BDDS). Our approach shows significant improvements over existing state-of-the-art on various backbone networks. Code is available at https://github.com/leolyj/SAN-SAW | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_Semantic-Aware_Domain_Generalized_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_Semantic-Aware_Domain_Generalized_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00822 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Semantic-Aware_Domain_Generalized_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Semantic-Aware_Domain_Generalized_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
TransVPR: Transformer-Based Place Recognition With Multi-Level Attention Aggregation | Ruotong Wang, Yanqing Shen, Weiliang Zuo, Sanping Zhou, Nanning Zheng | Visual place recognition is a challenging task for applications such as autonomous driving navigation and mobile robot localization. Distracting elements presenting in complex scenes often lead to deviations in the perception of visual place. To address this problem, it is crucial to integrate information from only task-relevant regions into image representations. In this paper, we introduce a novel holistic place recognition model, TransVPR, based on vision Transformers. It benefits from the desirable property of the self-attention operation in Transformers which can naturally aggregate task-relevant features. Attentions from multiple levels of the Transformer, which focus on different regions of interest, are further combined to generate a global image representation. In addition, the output tokens from Transformer layers filtered by the fused attention mask are considered as key-patch descriptors, which are used to perform spatial matching to re-rank the candidates retrieved by the global image features. The whole model allows end-to-end training with a single objective and image-level supervision. TransVPR achieves state-of-the-art performance on several real-world benchmarks while maintaining low computational time and storage requirements. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_TransVPR_Transformer-Based_Place_Recognition_With_Multi-Level_Attention_Aggregation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_TransVPR_Transformer-Based_Place_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_TransVPR_Transformer-Based_Place_Recognition_With_Multi-Level_Attention_Aggregation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_TransVPR_Transformer-Based_Place_Recognition_With_Multi-Level_Attention_Aggregation_CVPR_2022_paper.html | CVPR 2022 | null |
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion | Peize Sun, Jinkun Cao, Yi Jiang, Zehuan Yuan, Song Bai, Kris Kitani, Ping Luo | A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is partially motivated by recent progress in both object detection and re-ID, and partially motivated by biases in existing tracking datasets, where most objects tend to have distinguishing appearance and re-ID models are sufficient for establishing associations. In response to such bias, we would like to re-emphasize that methods for multi-object tracking should also work when object appearance is not sufficiently discriminative. To this end, we propose a large-scale dataset for multi-human tracking, where humans have similar appearance, diverse motion and extreme articulation. As the dataset contains mostly group dancing videos, we name it "DanceTrack". We expect DanceTrack to provide a better platform to develop more MOT algorithms that rely less on visual discrimination and depend more on motion analysis. We benchmark several state-of-the-art trackers on our dataset and observe a significant performance drop on DanceTrack when compared against existing benchmarks. The dataset, project code and competition is released at: https://github.com/DanceTrack. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_DanceTrack_Multi-Object_Tracking_in_Uniform_Appearance_and_Diverse_Motion_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.14690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DanceTrack_Multi-Object_Tracking_in_Uniform_Appearance_and_Diverse_Motion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DanceTrack_Multi-Object_Tracking_in_Uniform_Appearance_and_Diverse_Motion_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Learning of Debiased Representations With Pseudo-Attributes | Seonguk Seo, Joon-Young Lee, Bohyung Han | The distributional shift issue between training and test sets is a critical challenge in machine learning, and is aggravated when models capture unintended decision rules with spurious correlations. Although existing works often handle this issue using human supervision, the availability of the proper annotations is impractical and even unrealistic. To better tackle this challenge, we propose a simple but effective debiasing technique in an unsupervised manner. Specifically, we perform clustering on the feature embedding space and identify pseudo-bias-attributes by taking advantage of the clustering results even without an explicit attribute supervision. Then, we employ a novel cluster-based reweighting scheme for learning debiased representation; this prevents minority groups from being ignored for minimizing the overall loss, which is desirable for worst-case generalization. The extensive experiments demonstrate the outstanding performance of our approach on multiple standard benchmarks, which is even as competitive as the supervised method. We plan to release the source code of our work for better reproducibility. | https://openaccess.thecvf.com/content/CVPR2022/papers/Seo_Unsupervised_Learning_of_Debiased_Representations_With_Pseudo-Attributes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Seo_Unsupervised_Learning_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2108.02943 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_Unsupervised_Learning_of_Debiased_Representations_With_Pseudo-Attributes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_Unsupervised_Learning_of_Debiased_Representations_With_Pseudo-Attributes_CVPR_2022_paper.html | CVPR 2022 | null |
Protecting Celebrities From DeepFake With Identity Consistency Transformer | Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Ting Zhang, Weiming Zhang, Nenghai Yu, Dong Chen, Fang Wen, Baining Guo | In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions. The Identity Consistency Transformer incorporates a consistency loss for identity consistency determination. We show that Identity Consistency Transformer exhibits superior generalization ability not only across different datasets but also across various types of image degradation forms found in real-world applications including deepfake videos. The Identity Consistency Transformer can be easily enhanced with additional identity information when such information is available, and for this reason it is especially well-suited for detecting face forgeries involving celebrities. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Protecting_Celebrities_From_DeepFake_With_Identity_Consistency_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Protecting_Celebrities_From_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01318 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Protecting_Celebrities_From_DeepFake_With_Identity_Consistency_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Protecting_Celebrities_From_DeepFake_With_Identity_Consistency_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness | Giulio Lovisotto, Nicole Finnie, Mauricio Munoz, Chaithanya Kumar Mummadi, Jan Hendrik Metzen | Neural architectures based on attention such as vision transformers are revolutionizing image recognition. Their main benefit is that attention allows reasoning about all parts of a scene jointly. In this paper, we show how the global reasoning of (scaled) dot-product attention can be the source of a major vulnerability when confronted with adversarial patch attacks. We provide a theoretical understanding of this vulnerability and relate it to an adversary's ability to misdirect the attention of all queries to a single key token under the control of the adversarial patch. We propose novel adversarial objectives for crafting adversarial patches which target this vulnerability explicitly. We show the effectiveness of the proposed patch attacks on popular image classification (ViTs and DeiTs) and object detection models (DETR). We find that adversarial patches occupying 0.5% of the input can lead to robust accuracies as low as 0% for ViT on ImageNet, and reduce the mAP of DETR on MS COCO to less than 3%. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lovisotto_Give_Me_Your_Attention_Dot-Product_Attention_Considered_Harmful_for_Adversarial_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lovisotto_Give_Me_Your_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lovisotto_Give_Me_Your_Attention_Dot-Product_Attention_Considered_Harmful_for_Adversarial_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lovisotto_Give_Me_Your_Attention_Dot-Product_Attention_Considered_Harmful_for_Adversarial_CVPR_2022_paper.html | CVPR 2022 | null |
TubeDETR: Spatio-Temporal Video Grounding With Transformers | Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid | We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query. This is a challenging task that requires the joint and efficient modeling of temporal, spatial and multi-modal interactions. To address this task, we propose TubeDETR, a transformer-based architecture inspired by the recent success of such models for text-conditioned object detection. Our model notably includes: (i) an efficient video and text encoder that models spatial multi-modal interactions over sparsely sampled frames and (ii) a space-time decoder that jointly performs spatio-temporal localization. We demonstrate the advantage of our proposed components through an extensive ablation study. We also evaluate our full approach on the spatio-temporal video grounding task and demonstrate improvements over the state of the art on the challenging VidSTG and HC-STVG benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_TubeDETR_Spatio-Temporal_Video_Grounding_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_TubeDETR_Spatio-Temporal_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_TubeDETR_Spatio-Temporal_Video_Grounding_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_TubeDETR_Spatio-Temporal_Video_Grounding_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning | Shyamgopal Karthik, Massimiliano Mancini, Zeynep Akata | The goal of open-world compositional zero-shot learning(OW-CZSL) is to recognize compositions of state and objects in images, given only a subset of them during training and no prior on the unseen compositions. In this setting, models operate on a huge output space, containing all possible state-object compositions. While previous works tackle the problem by learning embeddings for the compositions jointly,here we revisit a simple CZSL baseline and predict the primitives, i.e. states and objects, independently. To ensure that the model develops primitive-specific features, we equip the state and object classifiers with separate, non-linear feature extractors. Moreover, we estimate the feasibility of each composition through external knowledge, using this prior to remove unfeasible compositions from the output space.Finally, we propose a new setting, i.e. CZSL under partial supervision (pCZSL), where either only objects or state labels are available during training and we can use our prior to estimate the missing labels. Our model, Knowledge-Guided Simple Primitives (KG-SP), achieves the state of the art in both OW-CZSL and pCZSL, surpassing most recent competitors even when coupled with semi-supervised learning techniques | https://openaccess.thecvf.com/content/CVPR2022/papers/Karthik_KG-SP_Knowledge_Guided_Simple_Primitives_for_Open_World_Compositional_Zero-Shot_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Karthik_KG-SP_Knowledge_Guided_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Karthik_KG-SP_Knowledge_Guided_Simple_Primitives_for_Open_World_Compositional_Zero-Shot_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Karthik_KG-SP_Knowledge_Guided_Simple_Primitives_for_Open_World_Compositional_Zero-Shot_CVPR_2022_paper.html | CVPR 2022 | null |
SLIC: Self-Supervised Learning With Iterative Clustering for Human Action Videos | Salar Hosseini Khorasgani, Yuxuan Chen, Florian Shkurti | Self-supervised methods have significantly closed the gap with end-to-end supervised learning for image classification [13,24]. In the case of human action videos, however, where both appearance and motion are significant factors of variation, this gap remains significant [28,58]. One of the key reasons for this is that sampling pairs of similar video clips, a required step for many self-supervised contrastive learning methods, is currently done conservatively to avoid false positives. A typical assumption is that similar clips only occur temporally close within a single video, leading to insufficient examples of motion similarity. To mitigate this, we propose SLIC, a clustering-based self-supervised contrastive learning method for human action videos. Our key contribution is that we improve upon the traditional intra-video positive sampling by using iterative clustering to group similar video instances. This enables our method to leverage pseudo-labels from the cluster assignments to sample harder positives and negatives. SLIC outperforms state-of-the-art video retrieval baselines by +15.4% on top-1 recall on UCF101 and by +5.7% when directly transferred to HMDB51. With end-to-end finetuning for action classification, SLIC achieves 83.2% top-1 accuracy (+0.8%) on UCF101 and 54.5% on HMDB51 (+1.6%). SLIC is also competitive with the state-of-the-art in action classification after self-supervised pretraining on Kinetics400. | https://openaccess.thecvf.com/content/CVPR2022/papers/Khorasgani_SLIC_Self-Supervised_Learning_With_Iterative_Clustering_for_Human_Action_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Khorasgani_SLIC_Self-Supervised_Learning_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Khorasgani_SLIC_Self-Supervised_Learning_With_Iterative_Clustering_for_Human_Action_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Khorasgani_SLIC_Self-Supervised_Learning_With_Iterative_Clustering_for_Human_Action_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | Yiqing Shen, Yuyin Zhou, Lequan Yu | Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains challenging to deal with heterogeneous data clients, as the discrepant data distributions usually prevent the global model from delivering good generalization ability on each participating client. In this paper, we propose CD^2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. Different from previous works which establish layer-wise personalization to overcome the non-IID data across different clients, we make the first attempt at channel-wise assignment for model personalization, referred to as channel decoupling. To further facilitate the collaboration between private and shared weights, we propose a novel cyclic distillation scheme to impose a consistent regularization between the local and global model representations during the federation. Guided by the cyclical distillation, our channel decoupling framework can deliver more accurate and generalized results for different kinds of heterogeneity, such as feature skew, label distribution skew, and concept shift. Comprehensive experiments on four benchmarks, including natural image and medical image analysis tasks, demonstrate the consistent effectiveness of our method on both local and external validations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shen_CD2-pFed_Cyclic_Distillation-Guided_Channel_Decoupling_for_Model_Personalization_in_Federated_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_CD2-pFed_Cyclic_Distillation-Guided_Channel_Decoupling_for_Model_Personalization_in_Federated_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_CD2-pFed_Cyclic_Distillation-Guided_Channel_Decoupling_for_Model_Personalization_in_Federated_CVPR_2022_paper.html | CVPR 2022 | null |
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection | Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah | Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, anomaly detection is an open-set problem. However, some studies assimilate anomaly detection to action recognition. This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types. To this end, we propose UBnormal, a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, we make sure to include disjoint sets of anomaly types in our training and test collections of videos. To our knowledge, UBnormal is the first video anomaly detection benchmark to allow a fair head-to-head comparison between one-class open-set models and supervised closed-set models, as shown in our experiments. Moreover, we provide empirical evidence showing that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework on two prominent data sets, Avenue and ShanghaiTech. Our benchmark is freely available at https://github.com/lilygeorgescu/UBnormal. | https://openaccess.thecvf.com/content/CVPR2022/papers/Acsintoae_UBnormal_New_Benchmark_for_Supervised_Open-Set_Video_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Acsintoae_UBnormal_New_Benchmark_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.08644 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Acsintoae_UBnormal_New_Benchmark_for_Supervised_Open-Set_Video_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Acsintoae_UBnormal_New_Benchmark_for_Supervised_Open-Set_Video_Anomaly_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Beyond Cross-View Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image | Yujiao Shi, Hongdong Li | This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. Existing methods often treat this problem as cross-view image retrieval, and use learned deep features to match the ground-level query image to a partition (e.g., a small patch) of the satellite map. By these methods, the localization accuracy is limited by the partitioning density of the satellite map (often in the order of tens meters). Departing from the conventional wisdom of image retrieval, this paper presents a novel solution that can achieve highly-accurate localization. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Specifically, we design a two-branch CNN to extract robust features from the ground and satellite images, respectively. To bridge the vast cross-view domain gap, we resort to a Geometry Projection module that projects features from the satellite map to the ground-view, based on a relative camera pose. Aiming to minimize the differences between the projected features and the observed features, we employ a differentiable Levenberg-Marquardt (LM) module to search for the optimal camera pose iteratively. The entire pipeline is differentiable and runs end-to-end. Extensive experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method. Notably, e.g., starting from a coarse estimate of camera location within a wide region of 40m x 40m, with an 80% likelihood our method quickly reduces the lateral location error to be within 5m on a new KITTI cross-view dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_Beyond_Cross-View_Image_Retrieval_Highly_Accurate_Vehicle_Localization_Using_Satellite_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_Beyond_Cross-View_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Beyond_Cross-View_Image_Retrieval_Highly_Accurate_Vehicle_Localization_Using_Satellite_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Beyond_Cross-View_Image_Retrieval_Highly_Accurate_Vehicle_Localization_Using_Satellite_CVPR_2022_paper.html | CVPR 2022 | null |
Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation | An Xu, Wenqi Li, Pengfei Guo, Dong Yang, Holger R. Roth, Ali Hatamizadeh, Can Zhao, Daguang Xu, Heng Huang, Ziyue Xu | Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years as it can resolve the critical issues of insufficient data, data privacy, and training efficiency. However, there can be a generalization gap between the model trained from FL and the one from centralized training. This important issue comes from the non-iid data distribution of the local data in the participating clients and is well-known as client drift. In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. We also propose a novel personalized FL objective formulation and a new method SoftPull to solve it in our proposed framework FedSM. We conduct rigorous theoretical analysis to guarantee its convergence for optimizing the non-convex smooth objective function. Real-world medical image segmentation experiments using deep FL validate the motivations and effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Closing_the_Generalization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10144 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
AKB-48: A Real-World Articulated Object Knowledge Base | Liu Liu, Wenqiang Xu, Haoyuan Fu, Sucheng Qian, Qiaojun Yu, Yang Han, Cewu Lu | Human life is populated with articulated objects. A comprehensive understanding of articulated objects, namely appearance, structure, physics property, and semantics, will benefit many research communities. As current articulated object understanding solutions are usually based on synthetic object dataset with CAD models without physics properties, which prevent satisfied generalization from simulation to real-world applications in visual and robotics tasks. To bridge the gap, we present AKB-48: a large-scale Articulated object Knowledge Base which consists of 2,037 real-world 3D articulated object models of 48 categories. Each object is described by a knowledge graph ArtiKG. To build the AKB-48, we present a fast articulation knowledge modeling (FArM) pipeline, which can fulfill the ArtiKG for an articulated object within 10-15 minutes, and largely reduce the cost for object modeling in the real world. Using our dataset, we propose AKBNet, an integral pipeline for Category-level Visual Articulation Manipulation (C-VAM) task, in which we benchmark three sub-tasks, namely pose estimation, object reconstruction and manipulation. Dataset, codes, and models are publicly available at https://liuliu66.github.io/AKB-48. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_AKB-48_A_Real-World_Articulated_Object_Knowledge_Base_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_AKB-48_A_Real-World_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_AKB-48_A_Real-World_Articulated_Object_Knowledge_Base_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_AKB-48_A_Real-World_Articulated_Object_Knowledge_Base_CVPR_2022_paper.html | CVPR 2022 | null |
Style-ERD: Responsive and Coherent Online Motion Style Transfer | Tianxin Tao, Xiaohang Zhan, Zhongquan Chen, Michiel van de Panne | Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as real-time avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_Style-ERD_Responsive_and_Coherent_Online_Motion_Style_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tao_Style-ERD_Responsive_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Style-ERD_Responsive_and_Coherent_Online_Motion_Style_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Style-ERD_Responsive_and_Coherent_Online_Motion_Style_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy | Tong Zhang, Congpei Qiu, Wei Ke, Sabine Süsstrunk, Mathieu Salzmann | Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain different image information, e.g., background and small objects, and thus tends to restrain the diversity of the learned representations. In this work, we address this issue by introducing a new self-supervised learning strategy, LoGo, that explicitly reasons about Lo cal and G l o bal crops. To achieve view invariance, LoGo encourages similarity between global crops from the same image, as well as between a global and a local crop. However, to correctly encode the fact that the content of smaller crops may differ entirely, LoGo promotes two local crops to have dissimilar representations, while being close to global crops. Our LoGo strategy can easily be applied to existing SSL methods. Our extensive experiments on a variety of datasets and using different self-supervised learning frameworks validate its superiority over existing approaches. Noticeably, we achieve better results than supervised models on transfer learning when using only 1/10 of the data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Leverage_Your_Local_and_Global_Representations_A_New_Self-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Leverage_Your_Local_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17205 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Leverage_Your_Local_and_Global_Representations_A_New_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Leverage_Your_Local_and_Global_Representations_A_New_Self-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Stratified Transformer for 3D Point Cloud Segmentation | Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia | 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance. Specifically, we first put forward a novel key sampling strategy. For each query point, we sample nearby points densely and distant points sparsely as its keys in a stratified way, which enables the model to enlarge the effective receptive field and enjoy long-range contexts at a low computational cost. Also, to combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information, which facilitates convergence and boosts performance. Besides, we adopt contextual relative position encoding to adaptively capture position information. Finally, a memory-efficient implementation is introduced to overcome the issue of varying point numbers in each window. Extensive experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code is available at https://github.com/dvlab-research/Stratified-Transformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lai_Stratified_Transformer_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14508 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
NeRF in the Dark: High Dynamic Range View Synthesis From Noisy Raw Images | Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron | Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images have been processed by a lossy camera pipeline that smooths detail, clips highlights, and distorts the simple noise distribution of raw sensor data. We modify NeRF to instead train directly on linear raw images, preserving the scene's full dynamic range. By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In addition to changing the camera viewpoint, we can manipulate focus, exposure, and tonemapping after the fact. Although a single raw image appears significantly more noisy than a postprocessed one, we show that NeRF is highly robust to the zero-mean distribution of raw noise. When optimized over many noisy raw inputs (25-200), NeRF produces a scene representation so accurate that its rendered novel views outperform dedicated single and multi-image deep raw denoisers run on the same wide baseline input images. As a result, our method, which we call RawNeRF, can reconstruct scenes from extremely noisy images captured in near-darkness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mildenhall_NeRF_in_the_Dark_High_Dynamic_Range_View_Synthesis_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mildenhall_NeRF_in_the_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mildenhall_NeRF_in_the_Dark_High_Dynamic_Range_View_Synthesis_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mildenhall_NeRF_in_the_Dark_High_Dynamic_Range_View_Synthesis_From_CVPR_2022_paper.html | CVPR 2022 | null |
DArch: Dental Arch Prior-Assisted 3D Tooth Instance Segmentation With Weak Annotations | Liangdong Qiu, Chongjie Ye, Pei Chen, Yunbi Liu, Xiaoguang Han, Shuguang Cui | Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth centroid detection and tooth instance segmentation. Accurately detecting the tooth centroids can help locate the individual tooth, thus benefiting the segmentation. Thus, our DArch proposes to leverage the dental arch prior to assist the detection. Specifically, we firstly propose a coarse-to-fine method to estimate the dental arch, in which the dental arch is initially generated by Bezier curve regression and then a lightweight network is trained to refine it. With the estimated dental arch, we then propose a novel Arch-aware Point Sampling (APS) method to assist the tooth centroid proposal generation. Meantime, a segmentor is independently trained using a patch-based training strategy, aiming to segment a tooth instance from a 3D patch centered at the tooth centroid. Experimental results on 4,773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qiu_DArch_Dental_Arch_Prior-Assisted_3D_Tooth_Instance_Segmentation_With_Weak_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qiu_DArch_Dental_Arch_Prior-Assisted_3D_Tooth_Instance_Segmentation_With_Weak_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qiu_DArch_Dental_Arch_Prior-Assisted_3D_Tooth_Instance_Segmentation_With_Weak_CVPR_2022_paper.html | CVPR 2022 | null |
Task Decoupled Framework for Reference-Based Super-Resolution | Yixuan Huang, Xiaoyun Zhang, Yu Fu, Siheng Chen, Ya Zhang, Yan-Feng Wang, Dazhi He | Reference-based super-resolution(RefSR) has achieved impressive progress on the recovery of high-frequency details thanks to an additional reference high-resolution(HR) image input. Although the superiority compared with Single-Image Super-Resolution(SISR), existing RefSR methods easily result in the reference-underuse issue and the reference-misuse as shown in Fig.1. In this work, we deeply investigate the cause of the two issues and further propose a novel framework to mitigate them. Our studies find that the issues are mostly due to the improper coupled framework design of current methods. Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features. Inspired by this finding, we propose a novel framework, which decouples the two tasks of RefSR, eliminating the interference between the LR image and the reference image. The super-resolution task upsamples the LR image leveraging only the LR image itself. The texture transfer task extracts and transfers abundant textures from the reference image to the coarsely upsampled result of the super-resolution task. Extensive experiments demonstrate clear improvements in both quantitative and qualitative evaluations over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Task_Decoupled_Framework_for_Reference-Based_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Task_Decoupled_Framework_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Task_Decoupled_Framework_for_Reference-Based_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Task_Decoupled_Framework_for_Reference-Based_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Aug-NeRF: Training Stronger Neural Radiance Fields With Triple-Level Physically-Grounded Augmentations | Tianlong Chen, Peihao Wang, Zhiwen Fan, Zhangyang Wang | Neural Radiance Field (NeRF) regresses a neural parameterized scene by differentially rendering multi-view images with ground-truth supervision. However, when interpolating novel views, NeRF often yields inconsistent and visually non-smooth geometric results, which we consider as a generalization gap between seen and unseen views. Recent advances in convolutional neural networks have demonstrated the promise of advanced robust data augmentations, either random or learned, in enhancing both in-distribution and out-of-distribution generalization. Inspired by that, we propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regularizing the NeRF training. Particularly, our proposal learns to seamlessly blend worst-case perturbations into three distinct levels of the NeRF pipeline with physical grounds, including (1) the input coordinates, to simulate imprecise camera parameters at image capture; (2) intermediate features, to smoothen the intrinsic feature manifold; and (3) pre-rendering output, to account for the potential degradation factors in the multi-view image supervision. Extensive results demonstrate that Aug-NeRF effectively boosts NeRF performance in both novel view synthesis (up to 1.5 dB PSNR gain) and underlying geometry reconstruction. Furthermore, thanks to the implicit smooth prior injected by the triple-level augmentations, Aug-NeRF can even recover scenes from heavily corrupted images, a highly challenging setting untackled before. Our codes are available in https://github.com/VITA-Group/Aug-NeRF. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Aug-NeRF_Training_Stronger_Neural_Radiance_Fields_With_Triple-Level_Physically-Grounded_Augmentations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Aug-NeRF_Training_Stronger_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Aug-NeRF_Training_Stronger_Neural_Radiance_Fields_With_Triple-Level_Physically-Grounded_Augmentations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Aug-NeRF_Training_Stronger_Neural_Radiance_Fields_With_Triple-Level_Physically-Grounded_Augmentations_CVPR_2022_paper.html | CVPR 2022 | null |
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation | Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano | We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes in indoor environments and providing a total of 34 image pairs annotated with semi-dense, high-resolution ground-truth labels in the form of disparity maps. To tackle the task, we propose a deep learning architecture trained in a self-supervised manner by exploiting a further RGB camera, required only during training data acquisition. In this setup, we can conveniently learn cross-modal matching in the absence of ground-truth labels by distilling knowledge from an easier RGB-RGB matching task based on a collection of about 11K unlabeled image triplets. Experiments show that the proposed pipeline sets a good performance bar (1.16 pixels average registration error) for future research on this novel, challenging task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tosi_RGB-Multispectral_Matching_Dataset_Learning_Methodology_Evaluation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tosi_RGB-Multispectral_Matching_Dataset_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tosi_RGB-Multispectral_Matching_Dataset_Learning_Methodology_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tosi_RGB-Multispectral_Matching_Dataset_Learning_Methodology_Evaluation_CVPR_2022_paper.html | CVPR 2022 | null |
Id-Free Person Similarity Learning | Bing Shuai, Xinyu Li, Kaustav Kundu, Joseph Tighe | Learning a unified person detection and re-identification model is a key component of modern trackers. However, training such models usually relies on the availability of training images / videos that are manually labeled with both person boxes and their identities. In this work, we explore training such a model by only using person box annotations, thus removing the necessity of manually labeling a training dataset with additional person identity annotation as these are expensive to collect. To this end, we present a contrastive learning framework to learn person similarity without using manually labeled identity annotations. First, we apply image-level augmentation to images on public person detection datasets, based on which we learn a strong model for general person detection as well as for short-term person re-identification. To learn a model capable of longer-term re-identification, we leverage the natural appearance evolution of each person in videos to serve as instance-level appearance augmentation in our contrastive loss formulation. Without access to the target dataset or person identity annotation, our model achieves competitive results compared to existing fully-supervised state-of-the-art methods on both person search and person tracking tasks. Our model also shows promising results for saving the annotation cost that is needed to achieve a certain level of performance on the person search task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shuai_Id-Free_Person_Similarity_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shuai_Id-Free_Person_Similarity_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shuai_Id-Free_Person_Similarity_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shuai_Id-Free_Person_Similarity_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification | Wei Wu, Jiawei Liu, Kecheng Zheng, Qibin Sun, Zheng-Jun Zha | Image-to-video person re-identification aims to retrieve the same pedestrian as the image-based query from a video-based gallery set. Existing methods treat it as a cross-modality retrieval task and learn the common latent embeddings from image and video modalities, which are both less effective and efficient due to large modality gap and redundant feature learning by utilizing all video frames. In this work, we first regard this task as point-to-set matching problem identical to human decision process, and propose a novel Temporal Complementarity-Guided Reinforcement Learning (TCRL) approach for image-to-video person re-identification. TCRL employs deep reinforcement learning to make sequential judgments on dynamically selecting suitable amount of frames from gallery videos, and accumulate adequate temporal complementary information among these frames by the guidance of the query image, towards balancing efficiency and accuracy. Specifically, TCRL formulates point-to-set matching procedure as Markov decision process, where a sequential judgement agent measures the uncertainty between the query image and all historical frames at each time step, and verifies that sufficient complementary clues are accumulated for judgment (same or different) or one more frames are requested to assist judgment. Moreover, TCRL maintains a sequential feature extraction module with a complementary residual detector to dynamically suppress redundant salient regions and thoroughly mine diverse complementary clues among these selected frames for enhancing frame-level representation. Extensive experiments demonstrate the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Temporal_Complementarity-Guided_Reinforcement_Learning_for_Image-to-Video_Person_Re-Identification_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Temporal_Complementarity-Guided_Reinforcement_Learning_for_Image-to-Video_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Temporal_Complementarity-Guided_Reinforcement_Learning_for_Image-to-Video_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Globetrotter: Connecting Languages by Connecting Images | Dídac Surís, Dave Epstein, Carl Vondrick | Machine translation between many languages at once is highly challenging, since training with ground truth requires supervision between all language pairs, which is difficult to obtain. Our key insight is that, while languages may vary drastically, the underlying visual appearance of the world remains consistent. We introduce a method that uses visual observations to bridge the gap between languages, rather than relying on parallel corpora or topological properties of the representations. We train a model that aligns segments of text from different languages if and only if the images associated with them are similar and each image in turn is well-aligned with its textual description. We train our model from scratch on a new dataset of text in over fifty languages with accompanying images. Experiments show that our method outperforms previous work on unsupervised word and sentence translation using retrieval. | https://openaccess.thecvf.com/content/CVPR2022/papers/Suris_Globetrotter_Connecting_Languages_by_Connecting_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suris_Globetrotter_Connecting_Languages_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Globetrotter_Connecting_Languages_by_Connecting_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Globetrotter_Connecting_Languages_by_Connecting_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Fairness-Aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models | Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren | Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants equally from different demographic groups, and risk assessment systems must eliminate racism in criminal justice. Existing efforts towards the ethical development of AI systems have leveraged data science to mitigate biases in the training set or introduced fairness principles into the training process. For a deployed AI system, however, it may not allow for retraining or tuning in practice. By contrast, we propose a more flexible approach, i.e., fairness-aware adversarial perturbation (FAAP), which learns to perturb input data to blind deployed models on fairness-related features, e.g., gender and ethnicity. The key advantage is that FAAP does not modify deployed models in terms of parameters and structures. To achieve this, we design a discriminator to distinguish fairness-related attributes based on latent representations from deployed models. Meanwhile, a perturbation generator is trained against the discriminator, such that no fairness-related features could be extracted from perturbed inputs. Exhaustive experimental evaluation demonstrates the effectiveness and superior performance of the proposed FAAP. In addition, FAAP is validated on real-world commercial deployments (inaccessible to model parameters), which shows the transferability of FAAP, foreseeing the potential of black-box adaptation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Fairness-Aware_Adversarial_Perturbation_Towards_Bias_Mitigation_for_Deployed_Deep_Models_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01584 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Fairness-Aware_Adversarial_Perturbation_Towards_Bias_Mitigation_for_Deployed_Deep_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Fairness-Aware_Adversarial_Perturbation_Towards_Bias_Mitigation_for_Deployed_Deep_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models | Feng Cheng, Mingze Xu, Yuanjun Xiong, Hao Chen, Xinyu Li, Wei Li, Wei Xia | We propose a memory efficient method, named Stochastic Backpropagation (SBP), for training deep neural networks on videos. It is based on the finding that gradients from incomplete execution for backpropagation can still effectively train the models with minimal accuracy loss, which attributes to the high redundancy of video. SBP keeps all forward paths but randomly and independently removes the backward paths for each network layer in each training step. It reduces the GPU memory cost by eliminating the need to cache activation values corresponding to the dropped backward paths, whose amount can be controlled by an adjustable keep-ratio. Experiments show that SBP can be applied to a wide range of models for video tasks, leading to up to 80.0% GPU memory saving and 10% training speedup with less than 1% accuracy drop on action recognition and temporal action detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Stochastic_Backpropagation_A_Memory_Efficient_Strategy_for_Training_Video_Models_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16755 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Stochastic_Backpropagation_A_Memory_Efficient_Strategy_for_Training_Video_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Stochastic_Backpropagation_A_Memory_Efficient_Strategy_for_Training_Video_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Semantic-Shape Adaptive Feature Modulation for Semantic Image Synthesis | Zhengyao Lv, Xiaoming Li, Zhenxing Niu, Bing Cao, Wangmeng Zuo | Recent years have witnessed substantial progress in semantic image synthesis, it is still challenging in synthesizing photo-realistic images with rich details. Most previous methods focus on exploiting the given semantic map, which just captures an object-level layout for an image. Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly inferred from an object's shape. In order to exploit the part-level layouts, we propose a Shape-aware Position Descriptor (SPD) to describe each pixel's positional feature, where object shape is explicitly encoded into the SPD feature. Furthermore, a Semantic-shape Adaptive Feature Modulation (SAFM) block is proposed to combine the given semantic map and our positional features to produce adaptively modulated features. Extensive experiments demonstrate that the proposed SPD and SAFM significantly improve the generation of objects with rich details. Moreover, our method performs favorably against the SOTA methods in terms of quantitative and qualitative evaluation. The source code and model are available at https://github.com/cszy98/SAFM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lv_Semantic-Shape_Adaptive_Feature_Modulation_for_Semantic_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lv_Semantic-Shape_Adaptive_Feature_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16898 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lv_Semantic-Shape_Adaptive_Feature_Modulation_for_Semantic_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lv_Semantic-Shape_Adaptive_Feature_Modulation_for_Semantic_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Egocentric Scene Understanding via Multimodal Spatial Rectifier | Tien Do, Khiem Vuong, Hyun Soo Park | In this paper, we study a problem of egocentric scene understanding, i.e., predicting depths and surface normals from an egocentric image. Egocentric scene understanding poses unprecedented challenges: (1) due to large head movements, the images are taken from non-canonical viewpoints (i.e., tilted images) where existing models of geometry prediction do not apply; (2) dynamic foreground objects including hands constitute a large proportion of visual scenes. These challenges limit the performance of the existing models learned from large indoor datasets, such as ScanNet and NYUv2, which comprise predominantly upright images of static scenes. We present a multimodal spatial rectifier that stabilizes the egocentric images to a set of reference directions, which allows learning a coherent visual representation. Unlike unimodal spatial rectifier that often produces excessive perspective warp for egocentric images, the multimodal spatial rectifier learns from multiple directions that can minimize the impact of the perspective warp. To learn visual representations of the dynamic foreground objects, we present a new dataset called EDINA (Egocentric Depth on everyday INdoor Activities) that comprises more than 500K synchronized RGBD frames and gravity directions. Equipped with the multimodal spatial rectifier and the EDINA dataset, our proposed method on single-view depth and surface normal estimation significantly outperforms the baselines not only on our EDINA dataset, but also on other popular egocentric datasets, such as First Person Hand Action (FPHA) and EPIC-KITCHENS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Do_Egocentric_Scene_Understanding_via_Multimodal_Spatial_Rectifier_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Do_Egocentric_Scene_Understanding_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Do_Egocentric_Scene_Understanding_via_Multimodal_Spatial_Rectifier_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Do_Egocentric_Scene_Understanding_via_Multimodal_Spatial_Rectifier_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels | Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, Xinyi Le | The crux of semi-supervised semantic segmentation is to assign pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability. We argue that every pixel matters to the model training. Intuitively, an unreliable prediction may get confused among the top classes (i.e., those with the highest probabilities), however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative sample to those most unlikely categories. Based on this insight, we develop an effective pipeline to make sufficient use of unlabeled data. We first separate reliable and unreliable pixels via the predicted entropy map, then push each unreliable pixel to a category-wise queue that consists of negative samples, and finally train the model with all candidate pixels. Considering the training evolution, where the prediction becomes more and more accurate, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Semi-Supervised_Semantic_Segmentation_Using_Unreliable_Pseudo-Labels_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Semi-Supervised_Semantic_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03884 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Semi-Supervised_Semantic_Segmentation_Using_Unreliable_Pseudo-Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Semi-Supervised_Semantic_Segmentation_Using_Unreliable_Pseudo-Labels_CVPR_2022_paper.html | CVPR 2022 | null |
Day-to-Night Image Synthesis for Training Nighttime Neural ISPs | Abhijith Punnappurath, Abdullah Abuolaim, Abdelrahman Abdelhamed, Alex Levinshtein, Michael S. Brown | Many flagship smartphone cameras now use a dedicated neural image signal processor (ISP) to render noisy raw sensor images to the final processed output. Training nightmode ISP networks relies on large-scale datasets of image pairs with: (1) a noisy raw image captured with a short exposure and a high ISO gain; and (2) a ground truth low-noise raw image captured with a long exposure and low ISO that has been rendered through the ISP. Capturing such image pairs is tedious and time-consuming, requiring careful setup to ensure alignment between the image pairs. In addition, ground truth images are often prone to motion blur due to the long exposure. To address this problem, we propose a method that synthesizes nighttime images from daytime images. Daytime images are easy to capture, exhibit low-noise (even on smartphone cameras) and rarely suffer from motion blur. We outline a processing framework to convert daytime raw images to have the appearance of realistic nighttime raw images with different levels of noise. Our procedure allows us to easily produce aligned noisy and clean nighttime image pairs. We show the effectiveness of our synthesis framework by training neural ISPs for nightmode rendering. Furthermore, we demonstrate that using our synthetic nighttime images together with small amounts of real data (e.g., 5% to 10%) yields performance almost on par with training exclusively on real nighttime images. Our dataset and code are available at https://github.com/SamsungLabs/day-to-night. | https://openaccess.thecvf.com/content/CVPR2022/papers/Punnappurath_Day-to-Night_Image_Synthesis_for_Training_Nighttime_Neural_ISPs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Punnappurath_Day-to-Night_Image_Synthesis_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Punnappurath_Day-to-Night_Image_Synthesis_for_Training_Nighttime_Neural_ISPs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Punnappurath_Day-to-Night_Image_Synthesis_for_Training_Nighttime_Neural_ISPs_CVPR_2022_paper.html | CVPR 2022 | null |
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