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DIFNet: Boosting Visual Information Flow for Image Captioning | Mingrui Wu, Xuying Zhang, Xiaoshuai Sun, Yiyi Zhou, Chao Chen, Jiaxin Gu, Xing Sun, Rongrong Ji | Current Image captioning (IC) methods predict textual words sequentially based on the input visual information from the visual feature extractor and the partially generated sentence information. However, for most cases, the partially generated sentence may dominate the target word prediction due to the insufficiency of visual information, making the generated descriptions irrelevant to the content of the given image. In this paper, we propose a Dual Information Flow Network (DIFNet) to address this issue, which takes segmentation feature as another visual information source to enhance the contribution of visual information for prediction. To maximize the use of two information flows, we also propose an effective feature fusion module termed Iterative Independent Layer Normalization (IILN) which can condense the most relevant inputs while retraining modality-specific information in each flow. Experiments show that our method is able to enhance the dependence of prediction on visual information, making word prediction more focused on the visual content, and thus achieve new state-of-the-art performance on the MSCOCO dataset, e.g., 136.2 CIDEr on COCO Karpathy test split. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_DIFNet_Boosting_Visual_Information_Flow_for_Image_Captioning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_DIFNet_Boosting_Visual_Information_Flow_for_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_DIFNet_Boosting_Visual_Information_Flow_for_Image_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Object Localization As Domain Adaption | Lei Zhu, Qi She, Qian Chen, Yunfei You, Boyu Wang, Yanye Lu | Weakly supervised object localization (WSOL) focuses on localizing objects only with the supervision of image-level classification masks. Most previous WSOL methods follow the classification activation map (CAM) that localizes objects based on the classification structure with the multi-instance learning (MIL) mechanism. However, the MIL mechanism makes CAM only activate discriminative object parts rather than the whole object, weakening its performance for localizing objects. To avoid this problem, this work provides a novel perspective that models WSOL as a domain adaption (DA) task, where the score estimator trained on the source/image domain is tested on the target/pixel domain to locate objects. Under this perspective, a DA-WSOL pipeline is designed to better engage DA approaches into WSOL to enhance localization performance. It utilizes a proposed target sampling strategy to select different types of target samples. Based on these types of target samples, domain adaption localization (DAL) loss is elaborated. It aligns the feature distribution between the two domains by DA and makes the estimator perceive target domain cues by Universum regularization. Experiments show that our pipeline outperforms SOTA methods on multi benchmarks. Code are released at https://github.com/zh460045050/DA-WSOL_CVPR2022. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Weakly_Supervised_Object_Localization_As_Domain_Adaption_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Weakly_Supervised_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01714 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Weakly_Supervised_Object_Localization_As_Domain_Adaption_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Weakly_Supervised_Object_Localization_As_Domain_Adaption_CVPR_2022_paper.html | CVPR 2022 | null |
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation | Zhaoyang Zeng, Yongsheng Luo, Zhenhua Liu, Fengyun Rao, Dian Li, Weidong Guo, Zhen Wen | Multi-modal video similarity evaluation is important for video recommendation systems such as video de-duplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task. The Tencent-MVSE dataset contains video pairs similarity annotations, and diverse metadata including Chinese title, automatic speech recognition (ASR) text, as well as human-annotated categories/tags. We provide a simple baseline with a multi-modal Transformer architecture to perform supervised multi-modal video similarity evaluation. We also explore pre-training strategies to make use of the unpaired data. The whole dataset as well as our baseline will be released to promote the development of the multi-modal video similarity evaluation. The dataset has been released in https://tencent-mvse.github.io/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zeng_Tencent-MVSE_A_Large-Scale_Benchmark_Dataset_for_Multi-Modal_Video_Similarity_Evaluation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Tencent-MVSE_A_Large-Scale_Benchmark_Dataset_for_Multi-Modal_Video_Similarity_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Tencent-MVSE_A_Large-Scale_Benchmark_Dataset_for_Multi-Modal_Video_Similarity_Evaluation_CVPR_2022_paper.html | CVPR 2022 | null |
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation | Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan-Jakob Sonke, Efstratios Gavves | The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods implement such support/query interactions by solely leveraging \it plain operations -- e.g., cosine similarity and feature concatenation -- for segmenting the query objects. However, these interaction approaches usually cannot well capture the intrinsic object details in the query images that are widely encountered in FSS, e.g., if the query object to be segmented has holes and slots, inaccurate segmentation almost always happens. To this end, we propose a dynamic prototype convolution network (DPCN) to fully capture the aforementioned intrinsic details for accurate FSS. Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels. Moreover, we equip DPCN with a support activation module (SAM) and a feature filtering module (FFM) to generate pseudo mask and filter out background information for the query images, respectively. SAM and FFM together can mine enriched context information from the query features. Our DPCN is also flexible and efficient under the k-shot FSS setting. Extensive experiments on PASCAL-5^i and COCO-20^i show that DPCN yields superior performances under both 1-shot and 5-shot settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Dynamic_Prototype_Convolution_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Dynamic_Prototype_Convolution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Dynamic_Prototype_Convolution_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Dynamic_Prototype_Convolution_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching | Nicolas Donati, Etienne Corman, Maks Ovsjanikov | State-of-the-art fully intrinsic network for non-rigid shape matching are unable to disambiguate between shape inner symmetries. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on DiffusionNet, which makes our method robust to discretization changes, while adding a vector-field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors. Our source code is available at: https://github.com/nicolasdonati/DUO-FM | https://openaccess.thecvf.com/content/CVPR2022/papers/Donati_Deep_Orientation-Aware_Functional_Maps_Tackling_Symmetry_Issues_in_Shape_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Donati_Deep_Orientation-Aware_Functional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13453 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Donati_Deep_Orientation-Aware_Functional_Maps_Tackling_Symmetry_Issues_in_Shape_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Donati_Deep_Orientation-Aware_Functional_Maps_Tackling_Symmetry_Issues_in_Shape_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation | Zhiyuan Liang, Tiancai Wang, Xiangyu Zhang, Jian Sun, Jianbing Shen | Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network with coarse-grained (i.e.,point-, scribble-, and block-wise) supervisions, where only a small proportion of pixels are labeled in each image. In this paper, we propose a novel tree energy loss for SASS by providing semantic guidance for unlabeled pixels. The tree energy loss represents images as minimum spanning trees to model both low-level and high-level pair-wise affinities. By sequentially applying these affinities to the network prediction, soft pseudo labels for unlabeled pixels are generated in a coarse-to-fine manner, resulting in dynamic online self-training. The tree energy loss is effective and easy to be incorporated into existing frameworks by combining it with a traditional segmentation loss. Compared with previous SASS methods, our method requires no multi-stage training strategies, alternating optimization procedures, additional supervised data, or time-consuming post-processing while outperforming them in all types of supervised settings. Code is available at https://github.com/megvii-research/TreeEnergyLoss. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Tree_Energy_Loss_Towards_Sparsely_Annotated_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Tree_Energy_Loss_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10739 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Tree_Energy_Loss_Towards_Sparsely_Annotated_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Tree_Energy_Loss_Towards_Sparsely_Annotated_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Mr.BiQ: Post-Training Non-Uniform Quantization Based on Minimizing the Reconstruction Error | Yongkweon Jeon, Chungman Lee, Eulrang Cho, Yeonju Ro | Post-training quantization compresses a neural network within few hours with only a small unlabeled calibration set. However, so far it has been only discussed and empirically demonstrated in the context of uniform quantization on convolutional neural networks. We thus propose a new post-training non-uniform quantization method, called Mr.BiQ, allowing low bit-width quantization even on Transformer models. In particular, we leverage multi-level binarization for weights while allowing activations to be represented as various data formats (e.g., INT8, bfloat16, binary-coding, and FP32). Unlike conventional methods which optimize full-precision weights first, then decompose the weights into quantization parameters, Mr.BiQ recognizes the quantization parameters (i.e., scaling factors and bit-code) as directly and jointly learnable parameters during the optimization. To verify the superiority of the proposed quantization scheme, we test Mr.BiQ on various models including convolutional neural networks and Transformer models. According to experimental results, Mr.BiQ shows significant improvement in terms of accuracy when the bit-width of weights is equal to 2: up to 5.35 p.p. improvement in CNNs, up to 4.23 p.p. improvement in Vision Transformers, and up to 3.37 point improvement in Transformers for NLP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jeon_Mr.BiQ_Post-Training_Non-Uniform_Quantization_Based_on_Minimizing_the_Reconstruction_Error_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jeon_Mr.BiQ_Post-Training_Non-Uniform_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jeon_Mr.BiQ_Post-Training_Non-Uniform_Quantization_Based_on_Minimizing_the_Reconstruction_Error_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jeon_Mr.BiQ_Post-Training_Non-Uniform_Quantization_Based_on_Minimizing_the_Reconstruction_Error_CVPR_2022_paper.html | CVPR 2022 | null |
MatteFormer: Transformer-Based Image Matting via Prior-Tokens | GyuTae Park, SungJoon Son, JaeYoung Yoo, SeHo Kim, Nojun Kwak | In this paper, we propose a transformer-based image matting model called MatteFormer, which takes full advantage of trimap information in the transformer block. Our method first introduces a prior-token which is a global representation of each trimap region (e.g. foreground, background and unknown). These prior-tokens are used as global priors and participate in the self-attention mechanism of each block. Each stage of the encoder is composed of PAST (Prior-Attentive Swin Transformer) block, which is based on the Swin Transformer block, but differs in a couple of aspects: 1) It has PA-WSA (Prior-Attentive Window Self-Attention) layer, performing self-attention not only with spatial-tokens but also with prior-tokens. 2) It has prior-memory which saves prior-tokens accumulatively from the previous blocks and transfers them to the next block. We evaluate our MatteFormer on the commonly used image matting datasets: Composition-1k and Distinctions-646. Experiment results show that our proposed method achieves state-of-the-art performance with a large margin. Our codes are available at https://github.com/webtoon/matteformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_MatteFormer_Transformer-Based_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15662 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.html | CVPR 2022 | null |
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training | Xiao Lu, Yihong Cao, Sheng Liu, Chengjiang Long, Zipei Chen, Xuanyu Zhou, Yimin Yang, Chunxia Xiao | It is challenging to annotate large-scale datasets for supervised video shadow detection methods. Using a model trained on labeled images to the video frames directly may lead to high generalization error and temporal inconsistent results. In this paper, we address these challenges by proposing a Spatio-Temporal Interpolation Consistency Training (STICT) framework to rationally feed the unlabeled video frames together with the labeled images into an image shadow detection network training. Specifically, we propose the Spatial and Temporal ICT, in which we define two new interpolation schemes, i.e., the spatial interpolation and the temporal interpolation. We then derive the spatial and temporal interpolation consistency constraints accordingly for enhancing generalization in the pixel-wise classification task and for encouraging temporal consistent predictions, respectively. In addition, we design a Scale-Aware Network for multi-scale shadow knowledge learning in images, and propose a scale-consistency constraint to minimize the discrepancy among the predictions at different scales. Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset. Experimental results show that, even without video labels, our approach is better than most state of the art supervised, semi-supervised or unsupervised image/video shadow detection methods and other methods in related tasks. Code and dataset are available at https://github.com/yihong-97/STICT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Video_Shadow_Detection_via_Spatio-Temporal_Interpolation_Consistency_Training_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Video_Shadow_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Video_Shadow_Detection_via_Spatio-Temporal_Interpolation_Consistency_Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Video_Shadow_Detection_via_Spatio-Temporal_Interpolation_Consistency_Training_CVPR_2022_paper.html | CVPR 2022 | null |
Ranking Distance Calibration for Cross-Domain Few-Shot Learning | Pan Li, Shaogang Gong, Chengjie Wang, Yanwei Fu | Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are in different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more information in the target domain rather than to overly elaborate training strategies on the source domain as in many existing methods. Hence, we start from a generic representation pre-trained by a cross-entropy loss and a conventional distance-based classifier, along with an image retrieval view, to employ a re-ranking process to calibrate a target distance matrix by discovering the k-reciprocal neighbours within the task. Assuming the pre-trained representation is biased towards the source, we construct a non-linear subspace to minimise task-irrelevant features therewithin while keep more transferrable discriminative information by a hyperbolic tangent transformation. The calibrated distance in this target-aware non-linear sub-space is complementary to that in the pre-trained representation. To impose such distance calibration information onto the pre-trained representation, a Kullback-Leibler divergence loss is employed to gradually guide the model towards the calibrated distance-based distribution. Extensive evaluations on eight target domains show that this target ranking calibration process can improve conventional distance-based classifiers in few-shot learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Ranking_Distance_Calibration_for_Cross-Domain_Few-Shot_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Ranking_Distance_Calibration_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Ranking_Distance_Calibration_for_Cross-Domain_Few-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Ranking_Distance_Calibration_for_Cross-Domain_Few-Shot_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Robust and Accurate Superquadric Recovery: A Probabilistic Approach | Weixiao Liu, Yuwei Wu, Sipu Ruan, Gregory S. Chirikjian | Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few parameters. However, as the first and foremost step, recovering superquadrics accurately and robustly from 3D data still remains challenging. The existing methods are subject to local optima and sensitive to noise and outliers in real-world scenarios, resulting in frequent failure in capturing geometric shapes. In this paper, we propose the first probabilistic method to recover superquadrics from point clouds. Our method builds a Gaussian-uniform mixture model (GUM) on the parametric surface of a superquadric, which explicitly models the generation of outliers and noise. The superquadric recovery is formulated as a Maximum Likelihood Estimation (MLE) problem. We propose an algorithm, Expectation, Maximization, and Switching (EMS), to solve this problem, where: (1) outliers are predicted from the posterior perspective; (2) the superquadric parameter is optimized by the trust-region reflective algorithm; and (3) local optima are avoided by globally searching and switching among parameters encoding similar superquadrics. We show that our method can be extended to the multi-superquadrics recovery for complex objects. The proposed method outperforms the state-of-the-art in terms of accuracy, efficiency, and robustness on both synthetic and real-world datasets. The code is at http://github.com/bmlklwx/EMS-superquadric_fitting.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Robust_and_Accurate_Superquadric_Recovery_A_Probabilistic_Approach_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Robust_and_Accurate_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14517 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Robust_and_Accurate_Superquadric_Recovery_A_Probabilistic_Approach_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Robust_and_Accurate_Superquadric_Recovery_A_Probabilistic_Approach_CVPR_2022_paper.html | CVPR 2022 | null |
Zero-Shot Text-Guided Object Generation With Dream Fields | Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole | We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. Due to the scarcity of diverse, captioned 3D data, prior methods only generate objects from a handful of categories, such as ShapeNet. Instead, we guide generation with image-text models pre-trained on large datasets of captioned images from the web. Our method optimizes a Neural Radiance Field from many camera views so that rendered images score highly with a target caption according to a pre-trained CLIP model. To improve fidelity and visual quality, we introduce simple geometric priors, including sparsityinducing transmittance regularization, scene bounds, and new MLP architectures. In experiments, Dream Fields produce realistic, multi-view consistent object geometry and color from a variety of natural language captions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jain_Zero-Shot_Text-Guided_Object_Generation_With_Dream_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jain_Zero-Shot_Text-Guided_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01455 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jain_Zero-Shot_Text-Guided_Object_Generation_With_Dream_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jain_Zero-Shot_Text-Guided_Object_Generation_With_Dream_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Pixel Trajectories With Multiscale Contrastive Random Walks | Zhangxing Bian, Allan Jabri, Alexei A. Efros, Andrew Owens | A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence. Yet, approaches that dominate each space differ. We take a step towards bridging this gap by extending the recent contrastive random walk formulation to much more dense, pixel-level space-time graphs. The main contribution is introducing hierarchy into the search problem by computing the transition matrix in a coarse-to-fine manner, forming a multiscale contrastive random walk. This establishes a unified technique for self-supervised learning of optical flow, keypoint tracking, and video object segmentation. Experiments demonstrate that, for each of these tasks, our unified model achieves performance competitive with strong self-supervised approaches specific to that task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bian_Learning_Pixel_Trajectories_With_Multiscale_Contrastive_Random_Walks_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.08379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bian_Learning_Pixel_Trajectories_With_Multiscale_Contrastive_Random_Walks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bian_Learning_Pixel_Trajectories_With_Multiscale_Contrastive_Random_Walks_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Correlation Mining Network for Person Image Generation | Zijian Wang, Xingqun Qi, Kun Yuan, Muyi Sun | Person image generation aims to perform non-rigid deformation on source images, which generally requires unaligned data pairs for training. Recently, self-supervised methods express great prospects in this task by merging the disentangled representations for self-reconstruction. However, such methods fail to exploit the spatial correlation between the disentangled features. In this paper, we propose a Self-supervised Correlation Mining Network (SCM-Net) to rearrange the source images in the feature space, in which two collaborative modules are integrated, Decomposed Style Encoder (DSE) and Correlation Mining Module (CMM). Specifically, the DSE first creates unaligned pairs at the feature level. Then, the CMM establishes the spatial correlation field for feature rearrangement. Eventually, a translation module transforms the rearranged features to realistic results. Meanwhile, for improving the fidelity of cross-scale pose transformation, we propose a graph based Body Structure Retaining Loss (BSR Loss) to preserve reasonable body structures on half body to full body generation. Extensive experiments conducted on DeepFashion dataset demonstrate the superiority of our method compared with other supervised and unsupervised approaches. Furthermore, satisfactory results on face generation show the versatility of our method in other deformation tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Self-Supervised_Correlation_Mining_Network_for_Person_Image_Generation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.13307 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Correlation_Mining_Network_for_Person_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Correlation_Mining_Network_for_Person_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Grounding Answers for Visual Questions Asked by Visually Impaired People | Chongyan Chen, Samreen Anjum, Danna Gurari | Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar and different. We then evaluate the SOTA VQA and VQA-Grounding models and demonstrate that current SOTA algorithms often fail to identify the correct visual evidence where the answer is located. These models regularly struggle when the visual evidence occupies a small fraction of the image, for images that are higher quality, as well as for visual questions that require skills in text recognition. The dataset, evaluation server, and leaderboard all can be found at the following link: https://vizwiz.org/tasks-and-datasets/answer-grounding-for-vqa/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Grounding_Answers_for_Visual_Questions_Asked_by_Visually_Impaired_People_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Grounding_Answers_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.01993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Grounding_Answers_for_Visual_Questions_Asked_by_Visually_Impaired_People_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Grounding_Answers_for_Visual_Questions_Asked_by_Visually_Impaired_People_CVPR_2022_paper.html | CVPR 2022 | null |
Task Adaptive Parameter Sharing for Multi-Task Learning | Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, Stefano Soatto | Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models for each task is performant, but incurs a substantial memory cost. To efficiently learn multiple downstream tasks we introduce Task Adaptive Parameter Sharing (TAPS), a simple method for tuning a base model to a new task by adaptively modifying a small, task-specific subset of layers. This enables multi-task learning while minimizing the resources used and avoids catastrophic forgetting and competition between tasks. TAPS solves a joint optimization problem which determines both the layers that are shared with the base model and the value of the task-specific weights. Further, a sparsity penalty on the number of active layers promotes weight sharing with the base model. Compared to other methods, TAPS retains a high accuracy on the target tasks while still introducing only a small number of task-specific parameters. Moreover, TAPS is agnostic to the particular architecture used and requires only minor changes to the training scheme. We evaluate our method on a suite of fine-tuning tasks and architectures (ResNet, DenseNet, ViT) and show that it achieves state-of-the-art performance while being simple to implement. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wallingford_Task_Adaptive_Parameter_Sharing_for_Multi-Task_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wallingford_Task_Adaptive_Parameter_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16708 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wallingford_Task_Adaptive_Parameter_Sharing_for_Multi-Task_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wallingford_Task_Adaptive_Parameter_Sharing_for_Multi-Task_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Sparse Instance Activation for Real-Time Instance Segmentation | Tianheng Cheng, Xinggang Wang, Shaoyu Chen, Wenqiang Zhang, Qian Zhang, Chang Huang, Zhaoxiang Zhang, Wenyu Liu | In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to highlight informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly outperforms the counterparts in terms of speed and accuracy. Code and models are available at https://github.com/hustvl/SparseInst. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.12827 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Automatic Color Image Stitching Using Quaternion Rank-1 Alignment | Jiaxue Li, Yicong Zhou | Color image stitching is a challenging task in real-world applications. This paper first proposes a quaternion rank-1 alignment (QR1A) model for high-precision color image alignment. To solve the optimization problem of QR1A, we develop a nested iterative algorithm under the framework of complex-valued alternating direction method of multipliers. To quantitatively evaluate image stitching performance, we propose a perceptual seam quality (PSQ) measure to calculate misalignments of local regions along the seamline. Using QR1A and PSQ, we further propose an automatic color image stitching (ACIS-QR1A) framework. In this framework, the automatic strategy and iterative learning strategy are developed to simultaneously learn the optimal seamline and local alignment. Extensive experiments on challenging datasets demonstrate that the proposed ACIS-QR1A is able to obtain high-quality stitched images under several difficult scenarios including large parallax, low textures, moving objects, large occlusions or/and their combinations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Automatic_Color_Image_Stitching_Using_Quaternion_Rank-1_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Automatic_Color_Image_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Automatic_Color_Image_Stitching_Using_Quaternion_Rank-1_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Automatic_Color_Image_Stitching_Using_Quaternion_Rank-1_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
VisualGPT: Data-Efficient Adaptation of Pretrained Language Models for Image Captioning | Jun Chen, Han Guo, Kai Yi, Boyang Li, Mohamed Elhoseiny | The limited availability of annotated data often hinders real-world applications of machine learning. To efficiently learn from small quantities of multimodal data, we leverage the linguistic knowledge from a large pre-trained language model (PLM) and quickly adapt it to new domains of image captioning. To effectively utilize a pretrained model, it is critical to balance the visual input and prior linguistic knowledge from pretraining. We propose VisualGPT, which employs a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the PLM with a small amount of in-domain image-text data. The proposed self-resurrecting activation unit produces sparse activations that prevent accidental overwriting of linguistic knowledge. When trained on 0.1%, 0.5% and 1% of the respective training sets, VisualGPT surpasses the best baseline by up to 10.0% CIDEr on MS COCO and 17.9% CIDEr on Conceptual Captions. Furthermore, VisualGPT achieves the state-of-the-art result on IU X-ray, a medical report generation dataset. Our code is available at https://github.com/Vision-CAIR/VisualGPT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_VisualGPT_Data-Efficient_Adaptation_of_Pretrained_Language_Models_for_Image_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_VisualGPT_Data-Efficient_Adaptation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2102.10407 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_VisualGPT_Data-Efficient_Adaptation_of_Pretrained_Language_Models_for_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_VisualGPT_Data-Efficient_Adaptation_of_Pretrained_Language_Models_for_Image_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
ESCNet: Gaze Target Detection With the Understanding of 3D Scenes | Jun Bao, Buyu Liu, Jun Yu | This paper aims to address the single image gaze target detection problem. Conventional methods either focus on 2D visual cues or exploit additional depth information in a very coarse manner. In this work, we propose to explicitly and effectively model 3D geometry under challenging scenario where only 2D annotations are available. We first obtain 3D point clouds of given scene with estimated depth and reference objects. Then we figure out the front-most points in all possible 3D directions of given person. These points are later leveraged in our ESCNet model. Specifically, ESCNet consists of geometry and scene parsing modules. The former produces an initial heatmap inferring the probability that each front-most point has been looking at according to estimated 3D gaze direction. And the latter further explores scene contextual cues to regulate detection results. We validate our idea on two publicly available dataset, GazeFollow and VideoAttentionTarget, and demonstrate the state-of-the-art performance. Our method also beats the human in terms of AUC on GazeFollow. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bao_ESCNet_Gaze_Target_Detection_With_the_Understanding_of_3D_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bao_ESCNet_Gaze_Target_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_ESCNet_Gaze_Target_Detection_With_the_Understanding_of_3D_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_ESCNet_Gaze_Target_Detection_With_the_Understanding_of_3D_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Can You Spot the Chameleon? Adversarially Camouflaging Images From Co-Salient Object Detection | Ruijun Gao, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Huazhu Fu, Wei Feng, Yang Liu, Song Wang | Co-salient object detection (CoSOD) has recently achieved significant progress and played a key role in retrieval-related tasks. However, it inevitably poses an entirely new safety and security issue, i.e., highly personal and sensitive content can potentially be extracting by powerful CoSOD methods. In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack. Specially, given an image selected from a group of images containing some common and salient objects, we aim to generate an adversarial version that can mislead CoSOD methods to predict incorrect co-salient regions. Note that, compared with general white-box adversarial attacks for classification, this new task faces two additional challenges: (1) low success rate due to the diverse appearance of images in the group; (2) low transferability across CoSOD methods due to the considerable difference between CoSOD pipelines. To address these challenges, we propose the very first black-box joint adversarial exposure and noise attack (Jadena), where we jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function. Our method, without any information on the state-of-the-art CoSOD methods, leads to significant performance degradation on various co-saliency detection datasets and makes the co-salient objects undetectable. This can have strong practical benefits in properly securing the large number of personal photos currently shared on the Internet. Moreover, our method is potential to be utilized as a metric for evaluating the robustness of CoSOD methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_Can_You_Spot_the_Chameleon_Adversarially_Camouflaging_Images_From_Co-Salient_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Can_You_Spot_the_Chameleon_Adversarially_Camouflaging_Images_From_Co-Salient_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Can_You_Spot_the_Chameleon_Adversarially_Camouflaging_Images_From_Co-Salient_CVPR_2022_paper.html | CVPR 2022 | null |
Finding Badly Drawn Bunnies | Lan Yang, Kaiyue Pang, Honggang Zhang, Yi-Zhe Song | As lovely as bunnies are, your sketched version would probably not do it justice (Fig. 1). This paper recognises this very problem and studies sketch quality measurement for the first time -- letting you find these badly drawn ones. Our key discovery lies in exploiting the magnitude (L2 norm) of a sketch feature as a quantitative quality metric. We propose Geometry-Aware Classification Layer (GACL), a generic method that makes feature-magnitude-as-quality-metric possible and importantly does it without the need for specific quality annotations from humans. GACL sees feature magnitude and recognisability learning as a dual task, which can be simultaneously optimised under a neat cross-entropy classification loss. GACL is lightweight with theoretic guarantees and enjoys a nice geometric interpretation to reason its success. We confirm consistent quality agreements between our GACL-induced metric and human perception through a carefully designed human study. Last but not least, we demonstrate three practical sketch applications enabled for the first time using our quantitative quality metric. Code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Finding_Badly_Drawn_Bunnies_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Finding_Badly_Drawn_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Finding_Badly_Drawn_Bunnies_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Finding_Badly_Drawn_Bunnies_CVPR_2022_paper.html | CVPR 2022 | null |
Point2Cyl: Reverse Engineering 3D Objects From Point Clouds to Extrusion Cylinders | Mikaela Angelina Uy, Yen-Yu Chang, Minhyuk Sung, Purvi Goel, Joseph G. Lambourne, Tolga Birdal, Leonidas J. Guibas | We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential task to enable manipulation of the 3D data in shape editing software and thus expand their usages in many downstream applications. Particularly, the form of CAD models having a sequence of extrusion cylinders --- a 2D sketch plus an extrusion axis and range --- and their boolean combinations is not only widely used in the CAD community/software but also has great expressivity of shapes, compared to having limited types of primitives (e.g., planes, spheres, and cylinders). In this work, we introduce a neural network that solves the extrusion cylinder decomposition problem in a geometry-grounded way by first learning underlying geometric proxies. Precisely, our approach first predicts per-point segmentation, base/barrel labels and normals, then estimates for the underlying extrusion parameters in differentiable and closed-form formulations. Our experiments show that our approach demonstrates the best performance on two recent CAD datasets, Fusion Gallery and DeepCAD, and we further showcase our approach on reverse engineering and editing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Uy_Point2Cyl_Reverse_Engineering_3D_Objects_From_Point_Clouds_to_Extrusion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Uy_Point2Cyl_Reverse_Engineering_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09329 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Uy_Point2Cyl_Reverse_Engineering_3D_Objects_From_Point_Clouds_to_Extrusion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Uy_Point2Cyl_Reverse_Engineering_3D_Objects_From_Point_Clouds_to_Extrusion_CVPR_2022_paper.html | CVPR 2022 | null |
All-Photon Polarimetric Time-of-Flight Imaging | Seung-Hwan Baek, Felix Heide | Time-of-flight (ToF) sensors provide an image modality fueling applications across domains, including lidar in autonomous driving, robotics, and augmented reality. Conventional ToF imaging methods estimate the depth of a scene point by sending pulses of light into a scene and measuring the time of flight of the first arriving photons that are returned from the scene, the ones directly reflected from a scene surface without any temporal delay. As such, all photons following this first response are typically considered as unwanted noise, including multi-bounce and sub-surface scattering of real-world materials. While multi-bounce scene interreflections have been extensively in recent work on non-line-of-sight imaging, we investigate temporally resolved sub-surface scattering in this work. We depart from the principle of first arrival and instead propose an all-photon ToF imaging method relying on polarization changes that analyzes both first- and late-arriving photons for shape and material scene understanding. To this end, we propose a novel capture method, reflectance model, and a reconstruction algorithm that exploits the polarization state of light changes after reflection in addition to ToF information. The proposed temporal-polarimetric imaging method allows for accurate geometric and material information of the scene by utilizing all photons captured by the system, decoded by polarization cues, outperforming all tested existing methods in simulation and experimentally. | https://openaccess.thecvf.com/content/CVPR2022/papers/Baek_All-Photon_Polarimetric_Time-of-Flight_Imaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Baek_All-Photon_Polarimetric_Time-of-Flight_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Baek_All-Photon_Polarimetric_Time-of-Flight_Imaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Baek_All-Photon_Polarimetric_Time-of-Flight_Imaging_CVPR_2022_paper.html | CVPR 2022 | null |
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation | Wenhao Li, Hong Liu, Hao Tang, Pichao Wang, Luc Van Gool | Estimating 3D human poses from monocular videos is a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spatial and temporal relationships. However, those works ignore the fact that it is an inverse problem where multiple feasible solutions (i.e., hypotheses) exist. To relieve this limitation, we propose a Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses. In order to effectively model multi-hypothesis dependencies and build strong relationships across hypothesis features, the task is decomposed into three stages: (i) Generate multiple initial hypothesis representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis communication and aggregate the multi-hypothesis features to synthesize the final 3D pose. Through the above processes, the final representation is enhanced and the synthesized pose is much more accurate. Extensive experiments show that MHFormer achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP. Without bells and whistles, its performance surpasses the previous best result by a large margin of 3% on Human3.6M. Code and models are available at https://github.com/Vegetebird/MHFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_MHFormer_Multi-Hypothesis_Transformer_for_3D_Human_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_MHFormer_Multi-Hypothesis_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12707 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MHFormer_Multi-Hypothesis_Transformer_for_3D_Human_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MHFormer_Multi-Hypothesis_Transformer_for_3D_Human_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis | Tianhan Xu, Yasuhiro Fujita, Eiichi Matsumoto | We propose a new method for reconstructing controllable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene representation on the mesh surface points and signed distances from the surface of a human body mesh. We identify an indistinguishability issue that arises when a point in 3D space is mapped to its nearest surface point on a mesh for learning surface-aligned neural scene representation. To address this issue, we propose projecting a point onto a mesh surface using a barycentric interpolation with modified vertex normals. Experiments with the ZJU-MoCap and Human3.6M datasets show that our approach achieves a higher quality in a novel-view and novel-pose synthesis than existing methods. We also demonstrate that our method easily supports the control of body shape and clothes. Project page: https://pfnet-research.github.io/surface-aligned-nerf/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Surface-Aligned_Neural_Radiance_Fields_for_Controllable_3D_Human_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Surface-Aligned_Neural_Radiance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.01683 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Surface-Aligned_Neural_Radiance_Fields_for_Controllable_3D_Human_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Surface-Aligned_Neural_Radiance_Fields_for_Controllable_3D_Human_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Learning From Temporal Gradient for Semi-Supervised Action Recognition | Junfei Xiao, Longlong Jing, Lin Zhang, Ju He, Qi She, Zongwei Zhou, Alan Yuille, Yingwei Li | Semi-supervised video action recognition tends to enable deep neural networks to achieve remarkable performance even with very limited labeled data. However, existing methods are mainly transferred from current image-based methods (e.g., FixMatch). Without specifically utilizing the temporal dynamics and inherent multimodal attributes, their results could be suboptimal. To better leverage the encoded temporal information in videos, we introduce temporal gradient as an additional modality for more attentive feature extraction in this paper. To be specific, our method explicitly distills the fine-grained motion representations from temporal gradient (TG) and imposes consistency across different modalities (i.e., RGB and TG). The performance of semi-supervised action recognition is significantly improved without additional computation or parameters during inference. Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i.e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i.e., different ratios of labeled data). | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiao_Learning_From_Temporal_Gradient_for_Semi-Supervised_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiao_Learning_From_Temporal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13241 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Learning_From_Temporal_Gradient_for_Semi-Supervised_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Learning_From_Temporal_Gradient_for_Semi-Supervised_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Implicit Text-Guided 3D Shape Generation | Zhengzhe Liu, Yi Wang, Xiaojuan Qi, Chi-Wing Fu | In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable of producing high-fidelity shapes with colors that match the given text description. This work has several technical contributions. First, we decouple the shape and color predictions for learning features in both texts and shapes, and propose the word-level spatial transformer to correlate word features from text with spatial features from shape. Also, we design a cyclic loss to encourage consistency between text and shape, and introduce the shape IMLE to diversify the generated shapes. Further, we extend the framework to enable text-guided shape manipulation. Extensive experiments on the largest existing text-shape benchmark manifest the superiority of this work. The code and the models are available at https://github.com/liuzhengzhe/Towards-Implicit Text-Guided-Shape-Generation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Implicit_Text-Guided_3D_Shape_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Towards_Implicit_Text-Guided_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14622 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Implicit_Text-Guided_3D_Shape_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Implicit_Text-Guided_3D_Shape_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Audio-Driven Neural Gesture Reenactment With Video Motion Graphs | Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, Evangelos Kalogerakis | Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, we propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, we developed an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. Our system generates reenactments that are consistent with both the audio rhythms and the speech content. We evaluate our synthesized video quality quantitatively, qualitatively, and with user studies, demonstrating that our method produces videos of much higher quality and consistency with the target audio compared to previous work and baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Audio-Driven_Neural_Gesture_Reenactment_With_Video_Motion_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Audio-Driven_Neural_Gesture_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Audio-Driven_Neural_Gesture_Reenactment_With_Video_Motion_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Audio-Driven_Neural_Gesture_Reenactment_With_Video_Motion_Graphs_CVPR_2022_paper.html | CVPR 2022 | null |
SoftCollage: A Differentiable Probabilistic Tree Generator for Image Collage | Jiahao Yu, Li Chen, Mingrui Zhang, Mading Li | Image collage task aims to create an informative and visual-aesthetic visual summarization for an image collection. While several recent works exploit tree-based algorithm to preserve image content better, all of them resort to hand-crafted adjustment rules to optimize the collage tree structure, leading to the failure of fully exploring the structure space of collage tree. Our key idea is to soften the discrete tree structure space into a continuous probability space. We propose SoftCollage, a novel method that employs a neural-based differentiable probabilistic tree generator to produce the probability distribution of correlation-preserving collage tree conditioned on deep image feature, aspect ratio and canvas size. The differentiable characteristic allows us to formulate the tree-based collage generation as a differentiable process and directly exploit gradient to optimize the collage layout in the level of probability space in an end-to-end manner. To facilitate image collage research, we propose AIC, a large-scale public-available annotated dataset for image collage evaluation. Extensive experiments on the introduced dataset demonstrate the superior performance of the proposed method. Data and codes are available at https://github.com/ChineseYjh/SoftCollage. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_SoftCollage_A_Differentiable_Probabilistic_Tree_Generator_for_Image_Collage_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_SoftCollage_A_Differentiable_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_SoftCollage_A_Differentiable_Probabilistic_Tree_Generator_for_Image_Collage_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_SoftCollage_A_Differentiable_Probabilistic_Tree_Generator_for_Image_Collage_CVPR_2022_paper.html | CVPR 2022 | null |
Transforming Model Prediction for Tracking | Christoph Mayer, Martin Danelljan, Goutam Bhat, Matthieu Paul, Danda Pani Paudel, Fisher Yu, Luc Van Gool | Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function. While this inductive bias integrates valuable domain knowledge, it limits the expressivity of the tracking network. In this work, we therefore propose a tracker architecture employing a Transformer-based model prediction module. Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We further extend the model predictor to estimate a second set of weights that are applied for accurate bounding box regression. The resulting tracker relies on training and on test frame information in order to predict all weights transductively. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mayer_Transforming_Model_Prediction_for_Tracking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mayer_Transforming_Model_Prediction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11192 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mayer_Transforming_Model_Prediction_for_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mayer_Transforming_Model_Prediction_for_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
A Unified Framework for Implicit Sinkhorn Differentiation | Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers | The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning frameworks. To allow for an efficient training of respective neural networks, we propose an algorithm that obtains analytical gradients of a Sinkhorn layer via implicit differentiation. In comparison to prior work, our framework is based on the most general formulation of the Sinkhorn operator. It allows for any type of loss function, while both the target capacities and cost matrices are differentiated jointly. We further construct error bounds of the resulting algorithm for approximate inputs. Finally, we demonstrate that for a number of applications, simply replacing automatic differentiation with our algorithm directly improves the stability and accuracy of the obtained gradients. Moreover, we show that it is computationally more efficient, particularly when resources like GPU memory are scarce. | https://openaccess.thecvf.com/content/CVPR2022/papers/Eisenberger_A_Unified_Framework_for_Implicit_Sinkhorn_Differentiation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Eisenberger_A_Unified_Framework_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Eisenberger_A_Unified_Framework_for_Implicit_Sinkhorn_Differentiation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Eisenberger_A_Unified_Framework_for_Implicit_Sinkhorn_Differentiation_CVPR_2022_paper.html | CVPR 2022 | null |
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation | Tuo Cao, Fei Luo, Yanping Fu, Wenxiao Zhang, Shengjie Zheng, Chunxia Xiao | Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a twostage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages of estimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2)We leverage the uncertainty of the estimated depth map to improve accuracy and robustness of the output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_DGECN_A_Depth-Guided_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.09983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs With Language Structures via Dependency Relationships | Chao Lou, Wenjuan Han, Yuhuan Lin, Zilong Zheng | Understanding realistic visual scene images together with language descriptions is a fundamental task towards generic visual understanding. Previous works have shown compelling comprehensive results by building hierarchical structures for visual scenes (e.g., scene graphs) and natural languages (e.g., dependency trees), individually. However, how to construct a joint vision-language (VL) structure has barely been investigated. More challenging but worthwhile, we introduce a new task that targets on inducing such a joint VL structure in an unsupervised manner. Our goal is to bridge the visual scene graphs and linguistic dependency trees seamlessly. Due to the lack of VL structural data, we start by building a new dataset VLParse. Rather than using labor-intensive labeling from scratch, we propose an automatic alignment procedure to produce coarse structures followed by human refinement to produce high-quality ones. Moreover, we benchmark our dataset by proposing a contrastive learning (CL)-based framework VLGAE, short for Vision-Language Graph Autoencoder. Our model obtains superior performance on two derived tasks, i.e., language grammar induction and VL phrase grounding. Ablations show the effectiveness of both visual cues and dependency relationships on fine-grained VL structure construction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lou_Unsupervised_Vision-Language_Parsing_Seamlessly_Bridging_Visual_Scene_Graphs_With_Language_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.14260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lou_Unsupervised_Vision-Language_Parsing_Seamlessly_Bridging_Visual_Scene_Graphs_With_Language_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lou_Unsupervised_Vision-Language_Parsing_Seamlessly_Bridging_Visual_Scene_Graphs_With_Language_CVPR_2022_paper.html | CVPR 2022 | null |
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling | Dat Huynh, Jason Kuen, Zhe Lin, Jiuxiang Gu, Ehsan Elhamifar | Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing works first pretrain a model on captioned images covering many novel classes and then finetune it on limited base classes with mask annotations. However, the high-level textual information learned from caption pretraining alone cannot effectively encode the details required for pixel-wise segmentation. To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images. Thus, our framework is capable of labeling novel classes in captions via their word semantics to self-train a student model. To account for noises in pseudo masks, we design a robust student model that selectively distills mask knowledge by estimating the mask noise levels, hence mitigating the adverse impact of noisy pseudo masks. By extensive experiments, we show the effectiveness of our framework, where we significantly improve mAP score by 4.5% on MS-COCO and 5.1% on the large-scale Open Images & Conceptual Captions datasets compared to the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huynh_Open-Vocabulary_Instance_Segmentation_via_Robust_Cross-Modal_Pseudo-Labeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huynh_Open-Vocabulary_Instance_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12698 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huynh_Open-Vocabulary_Instance_Segmentation_via_Robust_Cross-Modal_Pseudo-Labeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huynh_Open-Vocabulary_Instance_Segmentation_via_Robust_Cross-Modal_Pseudo-Labeling_CVPR_2022_paper.html | CVPR 2022 | null |
Locality-Aware Inter- and Intra-Video Reconstruction for Self-Supervised Correspondence Learning | Liulei Li, Tianfei Zhou, Wenguan Wang, Lu Yang, Jianwu Li, Yi Yang | Our target is to learn visual correspondence from unlabeled videos. We develop LIIR, a locality-aware inter-and intra-video reconstruction framework that fills in three missing pieces, i.e., instance discrimination, location awareness, and spatial compactness, of self-supervised correspondence learning puzzle. First, instead of most existing efforts focusing on intra-video self-supervision only, we exploit cross video affinities as extra negative samples within a unified, inter-and intra-video reconstruction scheme. This enables instance discriminative representation learning by contrasting desired intra-video pixel association against negative inter-video correspondence. Second, we merge position information into correspondence matching, and design a position shifting strategy to remove the side-effect of position encoding during inter-video affinity computation, making our LIIR location-sensitive. Third, to make full use of the spatial continuity nature of video data, we impose a compactness-based constraint on correspondence matching, yielding more sparse and reliable solutions. The learned representation surpasses self-supervised state-of-the-arts on label propagation tasks including objects, semantic parts, and keypoints. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Locality-Aware_Inter-_and_Intra-Video_Reconstruction_for_Self-Supervised_Correspondence_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Locality-Aware_Inter-_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Locality-Aware_Inter-_and_Intra-Video_Reconstruction_for_Self-Supervised_Correspondence_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Locality-Aware_Inter-_and_Intra-Video_Reconstruction_for_Self-Supervised_Correspondence_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
A Versatile Multi-View Framework for LiDAR-Based 3D Object Detection With Guidance From Panoptic Segmentation | Hamidreza Fazlali, Yixuan Xu, Yuan Ren, Bingbing Liu | 3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation information to further guide the detection process. In this paper, we propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation. In our method, the 3D object detection backbone, which is in Bird's-Eye-View (BEV) plane, is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone. This enables the detection backbone to leverage multi-view information to address the shortcomings of each projection view. Furthermore, foreground semantic information is incorporated to ease the detection task by highlighting the locations of each object class in the feature maps. Finally, a new center density heatmap generated based on the instance-level information further guides the detection backbone by suggesting possible box center locations for objects in the BEV plane. Our method works with any BEV-based 3D object detection method, and as shown by extensive experiments on the nuScenes dataset, it provides significant performance gains. Notably, the proposed method based on a single-stage CenterPoint 3D object detection network achieved state-of-the-art performance on nuScenes 3D Detection Benchmark with 67.3 NDS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fazlali_A_Versatile_Multi-View_Framework_for_LiDAR-Based_3D_Object_Detection_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fazlali_A_Versatile_Multi-View_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02133 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fazlali_A_Versatile_Multi-View_Framework_for_LiDAR-Based_3D_Object_Detection_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fazlali_A_Versatile_Multi-View_Framework_for_LiDAR-Based_3D_Object_Detection_With_CVPR_2022_paper.html | CVPR 2022 | null |
Query and Attention Augmentation for Knowledge-Based Explainable Reasoning | Yifeng Zhang, Ming Jiang, Qi Zhao | Explainable visual question answering (VQA) models have been developed with neural modules and query-based knowledge incorporation to answer knowledge-requiring questions. Yet, most reasoning methods cannot effectively generate queries or incorporate external knowledge during the reasoning process, which may lead to suboptimal results. To bridge this research gap, we present Query and Attention Augmentation, a general approach that augments neural module networks to jointly reason about visual and external knowledge. To take both knowledge sources into account during reasoning, it parses the input question into a functional program with queries augmented through a novel reinforcement learning method, and jointly directs augmented attention to visual and external knowledge based on intermediate reasoning results. With extensive experiments on multiple VQA datasets, our method demonstrates significant performance, explainability, and generalizability over state-of-the-art models in answering questions requiring different extents of knowledge. Our source code is available at https://github.com/SuperJohnZhang/QAA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Query_and_Attention_Augmentation_for_Knowledge-Based_Explainable_Reasoning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Query_and_Attention_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Query_and_Attention_Augmentation_for_Knowledge-Based_Explainable_Reasoning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Query_and_Attention_Augmentation_for_Knowledge-Based_Explainable_Reasoning_CVPR_2022_paper.html | CVPR 2022 | null |
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality | Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, Candace Ross | We present a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning, which we call Winoground. Given two images and two captions, the goal is to match them correctly--but crucially, both captions contain a completely identical set of words, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of fine-grained tags to assist in analyzing model performance. We probe a diverse range of state-of-the-art vision and language models and find that, surprisingly, none of them do much better than chance. Evidently, these models are not as skilled at visio-linguistic compositional reasoning as we might have hoped. We perform an extensive analysis to obtain insights into how future work might try to mitigate these models' shortcomings. We aim for Winoground to serve as a useful evaluation set for advancing the state of the art and driving further progress in the field. The dataset is available at https://huggingface.co/datasets/facebook/winoground. | https://openaccess.thecvf.com/content/CVPR2022/papers/Thrush_Winoground_Probing_Vision_and_Language_Models_for_Visio-Linguistic_Compositionality_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Thrush_Winoground_Probing_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03162 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Thrush_Winoground_Probing_Vision_and_Language_Models_for_Visio-Linguistic_Compositionality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Thrush_Winoground_Probing_Vision_and_Language_Models_for_Visio-Linguistic_Compositionality_CVPR_2022_paper.html | CVPR 2022 | null |
RFNet: Unsupervised Network for Mutually Reinforcing Multi-Modal Image Registration and Fusion | Han Xu, Jiayi Ma, Jiteng Yuan, Zhuliang Le, Wei Liu | In this paper, we propose a novel method to realize multi-modal image registration and fusion in a mutually reinforcing framework, termed as RFNet. We handle the registration in a coarse-to-fine fashion. For the first time, we exploit the feedback of image fusion to promote the registration accuracy rather than treating them as two separate issues. The fine-registered results also improve the fusion performance. Specifically, for image registration, we solve the bottlenecks of defining registration metrics applicable for multi-modal images and facilitating the network convergence. The metrics are defined based on image translation and image fusion respectively in the coarse and fine stages. The convergence is facilitated by the designed metrics and a deformable convolution-based network. For image fusion, we focus on texture preservation, which not only increases the information amount and quality of fusion results but also improves the feedback of fusion results. The proposed method is evaluated on multi-modal images with large global parallaxes, images with local misalignments and aligned images to validate the performances of registration and fusion. The results in these cases demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_RFNet_Unsupervised_Network_for_Mutually_Reinforcing_Multi-Modal_Image_Registration_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_RFNet_Unsupervised_Network_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_RFNet_Unsupervised_Network_for_Mutually_Reinforcing_Multi-Modal_Image_Registration_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_RFNet_Unsupervised_Network_for_Mutually_Reinforcing_Multi-Modal_Image_Registration_and_CVPR_2022_paper.html | CVPR 2022 | null |
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection | Jiaqi Tang, Zhaoyang Liu, Chen Qian, Wayne Wu, Limin Wang | Generic event boundary detection is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. The main challenge of this task is perceiving various temporal variations of diverse event boundaries. To this end, this paper presents an effective and end-to-end learnable framework (DDM-Net). To tackle the diversity and complicated semantics of event boundaries, we make three notable improvements. First, we construct a feature bank to store multi-level features of space and time, prepared for difference calculation at multiple scales. Second, to alleviate inadequate temporal modeling of previous methods, we present dense difference maps (DDM) to comprehensively characterize the motion pattern. Finally, we exploit progressive attention on multi-level DDM to jointly aggregate appearance and motion clues. As a result, DDM-Net respectively achieves a significant boost of 14% and 8% on Kinetics-GEBD and TAPOS benchmark, and outperforms the top-1 winner solution of LOVEU Challenge@CVPR 2021 without bells and whistles. The state-of-the-art result demonstrates the effectiveness of richer motion representation and more sophisticated aggregation, in handling the diversity of generic event boundary detection. The code is made available at https://github.com/MCG-NJU/DDM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Progressive_Attention_on_Multi-Level_Dense_Difference_Maps_for_Generic_Event_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Progressive_Attention_on_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04771 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Progressive_Attention_on_Multi-Level_Dense_Difference_Maps_for_Generic_Event_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Progressive_Attention_on_Multi-Level_Dense_Difference_Maps_for_Generic_Event_CVPR_2022_paper.html | CVPR 2022 | null |
Interactron: Embodied Adaptive Object Detection | Klemen Kotar, Roozbeh Mottaghi | Over the years various methods have been proposed for the problem of object detection. Recently, we have witnessed great strides in this domain owing to the emergence of powerful deep neural networks. However, there are typically two main assumptions common among these approaches. First, the model is trained on a fixed training set and is evaluated on a pre-recorded test set. Second, the model is kept frozen after the training phase, so no further updates are performed after the training is finished. These two assumptions limit the applicability of these methods to real-world settings. In this paper, we propose Interactron, a method for adaptive object detection in an interactive setting, where the goal is to perform object detection in images observed by an embodied agent navigating in different environments. Our idea is to continue training during inference and adapt the model at test time without any explicit supervision via interacting with the environment. Our adaptive object detection model provides a 11.8 point improvement in AP (and 19.1 points in AP50) over DETR, a recent, high-performance object detector. Moreover, we show that our object detection model adapts to environments with completely different appearance characteristics, and its performance is on par with a model trained with full supervision for those environments. We will release the code to help ease future research in this domain. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kotar_Interactron_Embodied_Adaptive_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kotar_Interactron_Embodied_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.00660 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kotar_Interactron_Embodied_Adaptive_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kotar_Interactron_Embodied_Adaptive_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
3D Scene Painting via Semantic Image Synthesis | Jaebong Jeong, Janghun Jo, Sunghyun Cho, Jaesik Park | We propose a novel approach to 3D scene painting using a configurable 3D scene layout. Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene. We exploit an off-the-shelf 2D semantic image synthesis method to teach the 3D painting network without explicit color supervision. Experiments show that our approach produces images with geometrically correct structures and supports scene manipulation, such as the change of viewpoint, object poses, and painting style. Our approach provides rich controllability to synthesized images in the aspect of 3D geometry. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jeong_3D_Scene_Painting_via_Semantic_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jeong_3D_Scene_Painting_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jeong_3D_Scene_Painting_via_Semantic_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jeong_3D_Scene_Painting_via_Semantic_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
MeMOT: Multi-Object Tracking With Memory | Jiarui Cai, Mingze Xu, Wei Li, Yuanjun Xiong, Wei Xia, Zhuowen Tu, Stefano Soatto | We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span. This is realized by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects, and by adaptively referencing and aggregating useful information from the memory as needed. Our model, called MeMOT, consists of three main modules that are all Transformer-based: 1) Hypothesis Generation that produce object proposals in the current video frame; 2) Memory Encoding that extracts the core information from the memory for each tracked object; and 3) Memory Decoding that solves the object detection and data association tasks simultaneously for multi-object tracking. When evaluated on widely adopted MOT benchmark datasets, MeMOT observes very competitive performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_MeMOT_Multi-Object_Tracking_With_Memory_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_MeMOT_Multi-Object_Tracking_With_Memory_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_MeMOT_Multi-Object_Tracking_With_Memory_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Weakly Supervised Pre-Training of Visual Perception Models | Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross Girshick, Piotr Dollár, Laurens van der Maaten | Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent studies suggest that large-scale weakly supervised pre-training can outperform fully supervised approaches. This paper revisits weakly-supervised pre-training of models using hashtag supervision with modern versions of residual networks and the largest-ever dataset of images and corresponding hashtags. We study the performance of the resulting models in various transfer-learning settings including zero-shot transfer. We also compare our models with those obtained via large-scale self-supervised learning. We find our weakly-supervised models to be very competitive across all settings, and find they substantially outperform their self-supervised counterparts. We also include an investigation into whether our models learned potentially troubling associations or stereotypes. Overall, our results provide a compelling argument for the use of weakly supervised learning in the development of visual recognition systems. Our models, Supervised Weakly through hashtAGs (SWAG), are available publicly. | https://openaccess.thecvf.com/content/CVPR2022/papers/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Singh_Revisiting_Weakly_Supervised_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.08371 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Semantic Segmentation With Error Localization Network | Donghyeon Kwon, Suha Kwak | This paper studies semi-supervised learning of semantic segmentation, which assumes that only a small portion of training images are labeled and the others remain unlabeled. The unlabeled images are usually assigned pseudo labels to be used in training, which however often causes the risk of performance degradation due to the confirmation bias towards errors on the pseudo labels. We present a novel method that resolves this chronic issue of pseudo labeling. At the heart of our method lies error localization network (ELN), an auxiliary module that takes an image and its segmentation prediction as input and identifies pixels whose pseudo labels are likely to be wrong. ELN enables semi-supervised learning to be robust against inaccurate pseudo labels by disregarding label noises during training and can be naturally integrated with self-training and contrastive learning. Moreover, we introduce a new learning strategy for ELN that simulates plausible and diverse segmentation errors during training of ELN to enhance its generalization. Our method is evaluated on PASCAL VOC 2012 and Cityscapes, where it outperforms all existing methods in every evaluation setting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kwon_Semi-Supervised_Semantic_Segmentation_With_Error_Localization_Network_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kwon_Semi-Supervised_Semantic_Segmentation_With_Error_Localization_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kwon_Semi-Supervised_Semantic_Segmentation_With_Error_Localization_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Meta Convolutional Neural Networks for Single Domain Generalization | Chaoqun Wan, Xu Shen, Yonggang Zhang, Zhiheng Yin, Xinmei Tian, Feng Gao, Jianqiang Huang, Xian-Sheng Hua | In single domain generalization, models trained with data from only one domain are required to perform well on many unseen domains. In this paper, we propose a new model, termed meta convolutional neural network, to solve the single domain generalization problem in image recognition. The key idea is to decompose the convolutional features of images into meta features. Acting as "visual words", meta features are defined as universal and basic visual elements for image representations (like words for documents in language). Taking meta features as reference, we propose compositional operations to eliminate irrelevant features of local convolutional features by an addressing process and then to reformulate the convolutional feature maps as a composition of related meta features. In this way, images are universally coded without biased information from the unseen domain, which can be processed by following modules trained in the source domain. The compositional operations adopt a regression analysis technique to learn the meta features in an online batch learning manner. Extensive experiments on multiple benchmark datasets verify the superiority of the proposed model in improving single domain generalization ability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wan_Meta_Convolutional_Neural_Networks_for_Single_Domain_Generalization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wan_Meta_Convolutional_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wan_Meta_Convolutional_Neural_Networks_for_Single_Domain_Generalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wan_Meta_Convolutional_Neural_Networks_for_Single_Domain_Generalization_CVPR_2022_paper.html | CVPR 2022 | null |
Generalizing Gaze Estimation With Rotation Consistency | Yiwei Bao, Yunfei Liu, Haofei Wang, Feng Lu | Recent advances of deep learning-based approaches have achieved remarkable performance on appearance-based gaze estimation. However, due to the shortage of target domain data and absence of target labels, generalizing gaze estimation algorithm to unseen environments is still challenging. In this paper, we discover the rotation-consistency property in gaze estimation and introduce the 'sub-label' for unsupervised domain adaptation. Consequently, we propose the Rotation-enhanced Unsupervised Domain Adaptation (RUDA) for gaze estimation. First, we rotate the original images with different angles for training. Then we conduct domain adaptation under the constraint of rotation consistency. The target domain images are assigned with sub-labels, derived from relative rotation angles rather than untouchable real labels. With such sub-labels, we propose a novel distribution loss that facilitates the domain adaptation. We evaluate the RUDA framework on four cross-domain gaze estimation tasks. Experimental results demonstrate that it improves the performance over the baselines with gains ranging from 12.2% to 30.5%. Our framework has the potential to be used in other computer vision tasks with physical constraints. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bao_Generalizing_Gaze_Estimation_With_Rotation_Consistency_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_Generalizing_Gaze_Estimation_With_Rotation_Consistency_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_Generalizing_Gaze_Estimation_With_Rotation_Consistency_CVPR_2022_paper.html | CVPR 2022 | null |
Anomaly Detection via Reverse Distillation From One-Class Embedding | Hanqiu Deng, Xingyu Li | Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD. However, using similar or identical architectures to build the teacher and student models in previous studies hinders the diversity of anomalous representations. To tackle this problem, we propose a novel T-S model consisting of a teacher encoder and a student decoder and introduce a simple yet effective "reverse distillation" paradigm accordingly. Instead of receiving raw images directly, the student network takes teacher model's one-class embedding as input and targets to restore the teacher's multi-scale representations. Inherently, knowledge distillation in this study starts from abstract, high-level presentations to low-level features. In addition, we introduce a trainable one-class bottleneck embedding (OCBE) module in our T-S model. The obtained compact embedding effectively preserves essential information on normal patterns, but abandons anomaly perturbations. Extensive experimentation on AD and one-class novelty detection benchmarks shows that our method surpasses SOTA performance, demonstrating our proposed approach's effectiveness and generalizability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_Anomaly_Detection_via_Reverse_Distillation_From_One-Class_Embedding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Deng_Anomaly_Detection_via_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.10703 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Anomaly_Detection_via_Reverse_Distillation_From_One-Class_Embedding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Anomaly_Detection_via_Reverse_Distillation_From_One-Class_Embedding_CVPR_2022_paper.html | CVPR 2022 | null |
Fine-Grained Object Classification via Self-Supervised Pose Alignment | Xuhui Yang, Yaowei Wang, Ke Chen, Yong Xu, Yonghong Tian | Semantic patterns of fine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, distinctive details carried by local regions can be spatially distributed or even self-occluded, leading to a large variation on object representation. For discounting pose variations, this paper proposes to learn a novel graph based object representation to reveal a global configuration of local parts for self-supervised pose alignment across classes, which is employed as an auxiliary feature regularization on a deep representation learning network. Moreover, a coarse-to-fine supervision together with the proposed pose-insensitive constraint on shallow-to-deep sub-networks encourages discriminative features in a curriculum learning manner. We evaluate our method on three popular fine-grained object classification benchmarks, consistently achieving the state-of-the-art performance. Source codes are available at https://github.com/yangxh11/P2P-Net. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Fine-Grained_Object_Classification_via_Self-Supervised_Pose_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Fine-Grained_Object_Classification_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15987 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Fine-Grained_Object_Classification_via_Self-Supervised_Pose_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Fine-Grained_Object_Classification_via_Self-Supervised_Pose_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction | Chongyang Zhong, Lei Hu, Zihao Zhang, Yongjing Ye, Shihong Xia | Predicting future motion based on historical motion sequence is a fundamental problem in computer vision, and it has wide applications in autonomous driving and robotics. Some recent works have shown that Graph Convolutional Networks(GCN) are instrumental in modeling the relationship between different joints. However, considering the variants and diverse action types in human motion data, the cross-dependency of the spatio-temporal relationships will be difficult to depict due to the decoupled modeling strategy, which may also exacerbate the problem of insufficient generalization. Therefore, we propose the Spatio-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex spatio-temporal dependencies over diverse action types. Specifically, we adopt gating networks to enhance the generalization of GCN via the trainable adaptive adjacency matrix obtained by blending the candidate spatio-temporal adjacency matrices. Moreover, GAGCN addresses the cross-dependency of space and time by balancing the weights of spatio-temporal modeling and fusing the decoupled spatio-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW demonstrate that GAGCN achieves state-of-the-art performance in both short-term and long-term predictions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_Spatio-Temporal_Gating-Adjacency_GCN_for_Human_Motion_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_Spatio-Temporal_Gating-Adjacency_GCN_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Spatio-Temporal_Gating-Adjacency_GCN_for_Human_Motion_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Spatio-Temporal_Gating-Adjacency_GCN_for_Human_Motion_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
CellTypeGraph: A New Geometric Computer Vision Benchmark | Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht | Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cerrone_CellTypeGraph_A_New_Geometric_Computer_Vision_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cerrone_CellTypeGraph_A_New_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.08166 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cerrone_CellTypeGraph_A_New_Geometric_Computer_Vision_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cerrone_CellTypeGraph_A_New_Geometric_Computer_Vision_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Clustering Plotted Data by Image Segmentation | Tarek Naous, Srinjay Sarkar, Abubakar Abid, James Zou | Clustering is a popular approach to detecting patterns in unlabeled data. Existing clustering methods typically treat samples in a dataset as points in a metric space and compute distances to group together similar points. In this paper, we present a different way of clustering points in 2-dimensional space, inspired by how humans cluster data: by training neural networks to perform instance segmentation on plotted data. Our approach, Visual Clustering, has several advantages over traditional clustering algorithms: it is much faster than most existing clustering algorithms (making it suitable for very large datasets), it agrees strongly with human intuition for clusters, and it is by default hyperparameter free (although additional steps with hyperparameters can be introduced for more control of the algorithm). We describe the method and compare it to ten other clustering methods on synthetic data to illustrate its advantages and disadvantages. We then demonstrate how our approach can be extended to higher-dimensional data and illustrate its performance on real-world data. Our implementation of Visual Clustering is publicly available as a python package that can be installed and used on any dataset in a few lines of code. A demo on synthetic datasets is provided. | https://openaccess.thecvf.com/content/CVPR2022/papers/Naous_Clustering_Plotted_Data_by_Image_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2110.05187 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Naous_Clustering_Plotted_Data_by_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Naous_Clustering_Plotted_Data_by_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Accelerating Neural Network Optimization Through an Automated Control Theory Lens | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Accelerating_Neural_Network_Optimization_Through_an_Automated_Control_Theory_Lens_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Accelerating_Neural_Network_Optimization_Through_an_Automated_Control_Theory_Lens_CVPR_2022_paper.html | CVPR 2022 | null |
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding | Xun Long Ng, Kian Eng Ong, Qichen Zheng, Yun Ni, Si Yong Yeo, Jun Liu | Understanding animals' behaviors is significant for a wide range of applications. However, existing animal behavior datasets have limitations in multiple aspects, including limited numbers of animal classes, data samples and provided tasks, and also limited variations in environmental conditions and viewpoints. To address these limitations, we create a large and diverse dataset, Animal Kingdom, that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footages used in our dataset record different times of the day in extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, our dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes. Such a challenging and comprehensive dataset shall be able to facilitate the community to develop, adapt, and evaluate various types of advanced methods for animal behavior analysis. Moreover, we propose a Collaborative Action Recognition (CARe) model that learns general and specific features for action recognition with unseen new animals. This method achieves promising performance in our experiments. Our dataset can be found at https://sutdcv.github.io/Animal-Kingdom. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ng_Animal_Kingdom_A_Large_and_Diverse_Dataset_for_Animal_Behavior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ng_Animal_Kingdom_A_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08129 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ng_Animal_Kingdom_A_Large_and_Diverse_Dataset_for_Animal_Behavior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ng_Animal_Kingdom_A_Large_and_Diverse_Dataset_for_Animal_Behavior_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Learn Across Diverse Data Biases in Deep Face Recognition | Chang Liu, Xiang Yu, Yi-Hsuan Tsai, Masoud Faraki, Ramin Moslemi, Manmohan Chandraker, Yun Fu | Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data. However, the data used for training CNNs is often imbalanced. Prior works largely focus on the long-tailed nature of face datasets in data volume per identity, or focus on single bias variation. In this paper, we show that many bias variations such as ethnicity, head pose, occlusion and blur can jointly affect the accuracy significantly. We propose a sample level weighting approach termed Multi-variation Cosine Margin (MvCoM), to simultaneously consider the multiple variation factors, which orthogonally enhances the face recognition losses to incorporate the importance of training samples. Further, we leverage a learning to learn approach, guided by a held-out meta learning set and use an additive modeling to predict the MvCoM. Extensive experiments on challenging face recognition benchmarks demonstrate the advantages of our method in jointly handling imbalances due to multiple variations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_To_Learn_Across_Diverse_Data_Biases_in_Deep_Face_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_To_Learn_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Learn_Across_Diverse_Data_Biases_in_Deep_Face_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Learn_Across_Diverse_Data_Biases_in_Deep_Face_CVPR_2022_paper.html | CVPR 2022 | null |
Back to Reality: Weakly-Supervised 3D Object Detection With Shape-Guided Label Enhancement | Xiuwei Xu, Yifan Wang, Yu Zheng, Yongming Rao, Jie Zhou, Jiwen Lu | In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers). In order to remedy the information loss from box annotations to centers, our method, namely Back to Reality (BR), makes use of synthetic 3D shapes to convert the weak labels into fully-annotated virtual scenes as stronger supervision, and in turn utilizes the perfect virtual labels to complement and refine the real labels. Specifically, we first assemble 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we go back to reality by applying a virtual-to-real domain adaptation method, which refine the weak labels and additionally supervise the training of detector with the virtual scenes. With less than 5% of the labeling labor, we achieve comparable detection performance with some popular fully-supervised approaches on the widely used ScanNet dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Back_to_Reality_Weakly-Supervised_3D_Object_Detection_With_Shape-Guided_Label_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Back_to_Reality_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05238 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Back_to_Reality_Weakly-Supervised_3D_Object_Detection_With_Shape-Guided_Label_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Back_to_Reality_Weakly-Supervised_3D_Object_Detection_With_Shape-Guided_Label_CVPR_2022_paper.html | CVPR 2022 | null |
Long-Tail Recognition via Compositional Knowledge Transfer | Sarah Parisot, Pedro M. Esperança, Steven McDonagh, Tamas J. Madarasz, Yongxin Yang, Zhenguo Li | In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer. Our objective is to transfer knowledge acquired from information-rich common classes to semantically similar, and yet data-hungry, rare classes in order to obtain stronger tail class representations. We leverage the fact that class prototypes and learned cosine classifiers provide two different, complementary representations of class cluster centres in feature space, and use an attention mechanism to select and recompose learned classifiers features from common classes to obtain higher quality rare class representations. Our knowledge transfer process is training free, reducing overfitting risks, and can afford continual extension of classifiers to new classes. Experiments show that our approach can achieve significant performance boosts on rare classes while maintaining robust common class performance, outperforming directly comparable state-of-the-art models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Parisot_Long-Tail_Recognition_via_Compositional_Knowledge_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Parisot_Long-Tail_Recognition_via_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parisot_Long-Tail_Recognition_via_Compositional_Knowledge_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parisot_Long-Tail_Recognition_via_Compositional_Knowledge_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal Retrieval | Haoyu Ma, Handong Zhao, Zhe Lin, Ajinkya Kale, Zhangyang Wang, Tong Yu, Jiuxiang Gu, Sunav Choudhary, Xiaohui Xie | recommendation, and marketing services. Extensive efforts have been made to conquer the cross-modal retrieval problem in the general domain. When it comes to E-commerce, a common practice is to adopt the pretrained model and finetune on E-commerce data. Despite its simplicity, the performance is sub-optimal due to overlooking the uniqueness of E-commerce multimodal data. A few recent efforts have shown significant improvements over generic methods with customized designs for handling product images. Unfortunately, to the best of our knowledge, no existing method has addressed the unique challenges in the e-commerce language. This work studies the outstanding one, where it has a large collection of special meaning entities, e.g., "Dissel (brand)", "Top (category)", "relaxed (fit)" in the fashion clothing business. By formulating such out-of-distribution finetuning process in the Causal Inference paradigm, we view the erroneous semantics of these special entities as confounders to cause the retrieval failure. To rectify these semantics for aligning with e-commerce domain knowledge, we propose an intervention-based entity-aware contrastive learning framework with two modules, i.e., the Confounding Entity Selection Module and Entity-Aware Learning Module. Our method achieves competitive performance on the E-commerce benchmark Fashion-Gen. Particularly, in top-1 accuracy (R@1), we observe 10.3% and 10.5% relative improvements over the closest baseline in image-to-text and text-to-image retrievals, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_EI-CLIP_Entity-Aware_Interventional_Contrastive_Learning_for_E-Commerce_Cross-Modal_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_EI-CLIP_Entity-Aware_Interventional_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_EI-CLIP_Entity-Aware_Interventional_Contrastive_Learning_for_E-Commerce_Cross-Modal_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_EI-CLIP_Entity-Aware_Interventional_Contrastive_Learning_for_E-Commerce_Cross-Modal_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions | De'Aira Bryant, Siqi Deng, Nashlie Sephus, Wei Xia, Pietro Perona | Humans can perceive multiple expressions, each one with varying intensity, in the picture of a face. We propose a methodology for collecting and modeling multidimensional modulated expression annotations from human annotators. Our data reveals that the perception of some expressions can be quite different across observers; thus, our model is designed to represent ambiguity alongside intensity. An empirical exploration of how many dimensions are necessary to capture the perception of facial expression suggests six principal expression dimensions are sufficient. Using our method, we collected multidimensional modulated expression annotations for 1,000 images culled from the popular ExpW in-the-wild dataset. As a proof of principle of our improved measurement technique, we used these annotations to benchmark four public domain algorithms for automated facial expression prediction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bryant_Multi-Dimensional_Nuanced_and_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.html | CVPR 2022 | null |
PyMiceTracking: An Open-Source Toolbox for Real-Time Behavioral Neuroscience Experiments | Richardson Menezes, Aron de Miranda, Helton Maia | The development of computational tools allows the advancement of research in behavioral neuroscience and elevates the limits of experiment design. Many behavioral experiments need to determine the animal's position from its tracking, which is crucial for real-time decision-making and further analysis of experimental data. Modern experimental designs usually generate the recording of a large amount of data, requiring the development of automatic computational tools and intelligent algorithms for timely data acquisition and processing. The proposed tool in this study initially operates with the acquisition of images. Then the animal tracking step begins with background subtraction, followed by the animal contour detection and morphological operations to remove noise in the detected shapes. Finally, in the final stage of the algorithm, the principal components analysis (PCA) is applied in the obtained shape, resulting in the animal's gaze direction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Menezes_PyMiceTracking_An_Open-Source_Toolbox_for_Real-Time_Behavioral_Neuroscience_Experiments_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Menezes_PyMiceTracking_An_Open-Source_Toolbox_for_Real-Time_Behavioral_Neuroscience_Experiments_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Menezes_PyMiceTracking_An_Open-Source_Toolbox_for_Real-Time_Behavioral_Neuroscience_Experiments_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Taught Metric Learning Without Labels | Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak | We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo labels. At the heart of our framework lies an algorithm that investigates contexts of data on the embedding space to predict their class-equivalence relations as pseudo labels. The algorithm enables efficient end-to-end training since it demands no off-the-shelf module for pseudo labeling. Also, the class-equivalence relations provide rich supervisory signals for learning an embedding space. On standard benchmarks for metric learning, it clearly outperforms existing unsupervised learning methods and sometimes even beats supervised learning models using the same backbone network. It is also applied to semi-supervised metric learning as a way of exploiting additional unlabeled data, and achieves the state of the art by boosting performance of supervised learning substantially. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Self-Taught_Metric_Learning_Without_Labels_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Self-Taught_Metric_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.01903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Self-Taught_Metric_Learning_Without_Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Self-Taught_Metric_Learning_Without_Labels_CVPR_2022_paper.html | CVPR 2022 | null |
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition | Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, Christoph Feichtenhofer | While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30x longer than existing models with only 4.5 more compute; traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_MeMViT_Memory-Augmented_Multiscale_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.08383 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Fine-Grained Temporal Contrastive Learning for Weakly-Supervised Temporal Action Localization | Junyu Gao, Mengyuan Chen, Changsheng Xu | We target at the task of weakly-supervised action localization (WSAL), where only video-level action labels are available during model training. Despite the recent progress, existing methods mainly embrace a localization-by-classification paradigm and overlook the fruitful fine-grained temporal distinctions between video sequences, thus suffering from severe ambiguity in classification learning and classification-to-localization adaption. This paper argues that learning by contextually comparing sequence-to-sequence distinctions offers an essential inductive bias in WSAL and helps identify coherent action instances. Specifically, under a differentiable dynamic programming formulation, two complementary contrastive objectives are designed, including Fine-grained Sequence Distance (FSD) contrasting and Longest Common Subsequence (LCS) contrasting, where the first one considers the relations of various action/background proposals by using match, insert, and delete operators and the second one mines the longest common subsequences between two videos. Both contrasting modules can enhance each other and jointly enjoy the merits of discriminative action-background separation and alleviated task gap between classification and localization. Extensive experiments show that our method achieves state-of-the-art performance on two popular benchmarks. Our code is available at https://github.com/MengyuanChen21/CVPR2022-FTCL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_Fine-Grained_Temporal_Contrastive_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16800 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Fine-Grained_Temporal_Contrastive_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Fine-Grained_Temporal_Contrastive_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Embracing Single Stride 3D Object Detector With Sparse Transformer | Lue Fan, Ziqi Pang, Tianyuan Zhang, Yu-Xiong Wang, Hang Zhao, Feng Wang, Naiyan Wang, Zhaoxiang Zhang | In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Overlooking this difference, many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. In this paper, we start by rethinking how such multi-stride stereotype affects the LiDAR-based 3D object detectors. Our experiments point out that the downsampling operations bring few advantages, and lead to inevitable information loss. To remedy this issue, we propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network. Armed with transformers, our method addresses the problem of insufficient receptive field in single-stride architectures. It also cooperates well with the sparsity of point clouds and naturally avoids expensive computation. Eventually, our SST achieves state-of-the-art results on the large-scale Waymo Open Dataset. It is worth mentioning that our method can achieve exciting performance (83.8 LEVEL_1 AP on validation split) on small object (pedestrian) detection due to the characteristic of single stride. Our codes will be public soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Embracing_Single_Stride_3D_Object_Detector_With_Sparse_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_Embracing_Single_Stride_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.06375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Embracing_Single_Stride_3D_Object_Detector_With_Sparse_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Embracing_Single_Stride_3D_Object_Detector_With_Sparse_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Multidimensional Belief Quantification for Label-Efficient Meta-Learning | Deep Shankar Pandey, Qi Yu | Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples introduces uncertainty, and quantifying model confidence for few-shot predictions is essential for many critical domains. Furthermore, few-shot tasks used in meta training are usually sampled randomly from a task distribution for an iterative model update, leading to high labeling costs and computational overhead in meta-training. We propose a novel uncertainty-aware task selection model for label efficient meta-learning. The proposed model formulates a multidimensional belief measure, which can quantify the known uncertainty and lower bound the unknown uncertainty of any given task. Our theoretical result establishes an important relationship between the conflicting belief and the incorrect belief. The theoretical result allows us to estimate the total uncertainty of a task, which provides a principled criterion for task selection. A novel multi-query task formulation is further developed to improve both the computational and labeling efficiency of meta-learning. Experiments conducted over multiple real-world few-shot image classification tasks demonstrate the effectiveness of the proposed model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pandey_Multidimensional_Belief_Quantification_for_Label-Efficient_Meta-Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pandey_Multidimensional_Belief_Quantification_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12768 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pandey_Multidimensional_Belief_Quantification_for_Label-Efficient_Meta-Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pandey_Multidimensional_Belief_Quantification_for_Label-Efficient_Meta-Learning_CVPR_2022_paper.html | CVPR 2022 | null |
UTC: A Unified Transformer With Inter-Task Contrastive Learning for Visual Dialog | Cheng Chen, Zhenshan Tan, Qingrong Cheng, Xin Jiang, Qun Liu, Yudong Zhu, Xiaodong Gu | Visual Dialog aims to answer multi-round, interactive questions based on the dialog history and image content. Existing methods either consider answer ranking and generating individually or only weakly capture the relation across the two tasks implicitly by two separate models. The research on a universal framework that jointly learns to rank and generate answers in a single model is seldom explored. In this paper, we propose a contrastive learning-based framework UTC to unify and facilitate both discriminative and generative tasks in visual dialog with a single model. Specifically, considering the inherent limitation of the previous learning paradigm, we devise two inter-task contrastive losses i.e., context contrastive loss and answer contrastive loss to make the discriminative and generative tasks mutually reinforce each other. These two complementary contrastive losses exploit dialog context and target answer as anchor points to provide representation learning signals from different perspectives. We evaluate our proposed UTC on the VisDial v1.0 dataset, where our method outperforms the state-of-the-art on both discriminative and generative tasks and surpasses previous state-of-the-art generative methods by more than 2 absolute points on Recall@1. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_UTC_A_Unified_Transformer_With_Inter-Task_Contrastive_Learning_for_Visual_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.00423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_UTC_A_Unified_Transformer_With_Inter-Task_Contrastive_Learning_for_Visual_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_UTC_A_Unified_Transformer_With_Inter-Task_Contrastive_Learning_for_Visual_CVPR_2022_paper.html | CVPR 2022 | null |
Relieving Long-Tailed Instance Segmentation via Pairwise Class Balance | Yin-Yin He, Peizhen Zhang, Xiu-Shen Wei, Xiangyu Zhang, Jian Sun | Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. It causes severe biases of the head classes (with majority samples) against the tailed ones. This renders "how to appropriately define and alleviate the bias" one of the most important issues. Prior works mainly use label distribution or mean score information to indicate a coarse-grained bias. In this paper, we explore to excavate the confusion matrix, which carries the fine-grained misclassification details, to relieve the pairwise biases, generalizing the coarse one. To this end, we propose a novel Pairwise Class Balance (PCB) method, built upon a confusion matrix which is updated during training to accumulate the ongoing prediction preferences. PCB generates fightback soft labels for regularization during training. Besides, an iterative learning paradigm is developed to support a progressive and smooth regularization in such debiasing. PCB can be plugged and played to any existing methods as a complement. Experiments results on LVIS demonstrate that our method achieves state-of-the-art performance without bells and whistles. Superior results across various architectures show the generalization ability. The code and trained models are available at https://github.com/megvii-research/PCB. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Relieving_Long-Tailed_Instance_Segmentation_via_Pairwise_Class_Balance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Relieving_Long-Tailed_Instance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02784 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Relieving_Long-Tailed_Instance_Segmentation_via_Pairwise_Class_Balance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Relieving_Long-Tailed_Instance_Segmentation_via_Pairwise_Class_Balance_CVPR_2022_paper.html | CVPR 2022 | null |
Online Convolutional Re-Parameterization | Mu Hu, Junyi Feng, Jiashen Hua, Baisheng Lai, Jianqiang Huang, Xiaojin Gong, Xian-Sheng Hua | Structural re-parameterization has drawn increasing attention in various computer vision tasks. It aims at improving the performance of deep models without introducing any inference-time cost. Though efficient during inference, such models rely heavily on the complicated training-time blocks to achieve high accuracy, leading to large extra training cost. In this paper, we present online convolutional re-parameterization (OREPA), a two-stage pipeline, aiming to reduce the huge training overhead by squeezing the complex training-time block into a single convolution. To achieve this goal, we introduce a linear scaling layer for better optimizing the online blocks. Assisted with the reduced training cost, we also explore some more effective re-param components. Compared with the state-of-the-art re-param models, OREPA is able to save the training-time memory cost by about 70% and accelerate the training speed by around 2x. Meanwhile, equipped with OREPA, the models outperform previous methods on ImageNet by up to +0.6%. We also conduct experiments on object detection and semantic segmentation and show consistent improvements on the downstream tasks. Codes are available at https://github.com/JUGGHM/OREPA_CVPR2022. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Online_Convolutional_Re-Parameterization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Online_Convolutional_Re-Parameterization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00826 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Online_Convolutional_Re-Parameterization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Online_Convolutional_Re-Parameterization_CVPR_2022_paper.html | CVPR 2022 | null |
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning | Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip H.S. Torr, Song Bai, Vincent Y. F. Tan | Class Incremental Learning (CIL) aims at learning a classifier in a phase-by-phase manner, in which only data of a subset of the classes are provided at each phase. Previous works mainly focus on mitigating forgetting in phases after the initial one. However, we find that improving CIL at its initial phase is also a promising direction. Specifically, we experimentally show that directly encouraging CIL Learner at the initial phase to output similar representations as the model jointly trained on all classes can greatly boost the CIL performance. Motivated by this, we study the difference between a naively-trained initial-phase model and the oracle model. Specifically, since one major difference between these two models is the number of training classes, we investigate how such difference affects the model representations. We find that, with fewer training classes, the data representations of each class lie in a long and narrow region; with more training classes, the representations of each class scatter more uniformly. Inspired by this observation, we propose Class-wise Decorrelation (CwD) that effectively regularizes representations of each class to scatter more uniformly, thus mimicking the model jointly trained with all classes (i.e., the oracle model). Our CwD is simple to implement and easy to plug into existing methods. Extensive experiments on various benchmark datasets show that CwD consistently and significantly improves the performance of existing state-of-the-art methods by around 1% to 3%. Code: https://github.com/Yujun-Shi/CwD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_Mimicking_the_Oracle_An_Initial_Phase_Decorrelation_Approach_for_Class_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_Mimicking_the_Oracle_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04731 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Mimicking_the_Oracle_An_Initial_Phase_Decorrelation_Approach_for_Class_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Mimicking_the_Oracle_An_Initial_Phase_Decorrelation_Approach_for_Class_CVPR_2022_paper.html | CVPR 2022 | null |
RIDDLE: Lidar Data Compression With Range Image Deep Delta Encoding | Xuanyu Zhou, Charles R. Qi, Yin Zhou, Dragomir Anguelov | Lidars are depth measuring sensors widely used in autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high costs in data storage and transmission. While lidar data can be represented as two interchangeable representations: 3D point clouds and range images, most previous work focus on compressing the generic 3D point clouds. In this work, we show that directly compressing the range images can leverage the lidar scanning pattern, compared to compressing the unprojected point clouds. We propose a novel data-driven range image compression algorithm, named RIDDLE (Range Image Deep DeLta Encoding). At its core is a deep model that predicts the next pixel value in a raster scanning order, based on contextual laser shots from both the current and past scans (represented as a 4D point cloud of spherical coordinates and time). The deltas between predictions and original values can then be compressed by entropy encoding. Evaluated on the Waymo Open Dataset and KITTI, our method demonstrates significant improvement in the compression rate (under the same distortion) compared to widely used point cloud and range image compression algorithms as well as recent deep methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_RIDDLE_Lidar_Data_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.html | CVPR 2022 | null |
RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition | Jun Chen, Aniket Agarwal, Sherif Abdelkarim, Deyao Zhu, Mohamed Elhoseiny | The visual relationship recognition (VRR) task aims at understanding the pairwise visual relationships between interacting objects in an image. These relationships typically have a long-tail distribution due to their compositional nature. This problem gets more severe when the vocabulary becomes large, rendering this task very challenging. This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR. The method, called RelTransformer, represents each im- age as a fully-connected scene graph and restructures the whole scene into the relation-triplet and global-scene contexts. It directly passes the message from each element in the relation-triplet and global-scene contexts to the target relation via self-attention. We also design a learnable memory to augment the long-tail relation representation learning. Through extensive experiments, we find that our model generalizes well on many VRR benchmarks. Our model outperforms the best-performing models on two large-scale long-tail VRR benchmarks, VG8K-LT (+2.0% overall acc) and GQA-LT (+26.0% overall acc), both having a highly skewed distribution towards the tail. It also achieves strong results on the VG200 relation detection task. Our code is available at https://github.com/Vision-CAIR/ RelTransformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_RelTransformer_A_Transformer-Based_Long-Tail_Visual_Relationship_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_RelTransformer_A_Transformer-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.11934 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_RelTransformer_A_Transformer-Based_Long-Tail_Visual_Relationship_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_RelTransformer_A_Transformer-Based_Long-Tail_Visual_Relationship_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks | Miao Yin, Yang Sui, Wanzhao Yang, Xiao Zang, Yu Gong, Bo Yuan | High-order decomposition is a widely used model compression approach towards compact convolutional neural networks (CNNs). However, many of the existing solutions, though can efficiently reduce CNN model sizes, are very difficult to bring considerable saving for computational costs, especially when the compression ratio is not huge, thereby causing the severe computation inefficiency problem. To overcome this challenge, in this paper we propose efficient High-Order DEcomposed Convolution (HODEC). By performing systematic explorations on the underlying reason and mitigation strategy for the computation inefficiency, we develop a new decomposition and computation-efficient execution scheme, enabling simultaneous reductions in computational and storage costs. To demonstrate the effectiveness of HODEC, we perform empirical evaluations for various CNN models on different datasets. HODEC shows consistently outstanding compression and acceleration performance. For ResNet-56 on CIFAR-10 dataset, HODEC brings 67% fewer model parameters and 62% fewer FLOPs with 1.17% accuracy increase than the baseline. For ResNet-50 on ImageNet dataset, HODEC achieves 63% FLOPs reduction with 0.31% accuracy increase than the uncompressed model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yin_HODEC_Towards_Efficient_High-Order_DEcomposed_Convolutional_Neural_Networks_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_HODEC_Towards_Efficient_High-Order_DEcomposed_Convolutional_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_HODEC_Towards_Efficient_High-Order_DEcomposed_Convolutional_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior | Ruibo Li, Chi Zhang, Guosheng Lin, Zhe Wang, Chunhua Shen | In this work, we focus on scene flow learning on point clouds in a self-supervised manner. A real-world scene can be well modeled as a collection of rigidly moving parts, therefore its scene flow can be represented as a combination of rigid motion of each part. Inspired by this observation, we propose to generate pseudo scene flow for self-supervised learning based on piecewise rigid motion estimation, in which the source point cloud is decomposed into a set of local regions and each region is treated as rigid. By rigidly aligning each region with its potential counterpart in the target point cloud, we obtain a region-specific rigid transformation to represent the flow, which together constitutes the pseudo scene flow labels of the entire scene to enable network training. Compared with most existing approaches relying on point-wise similarities for point matching, our method explicitly enforces region-wise rigid alignments, yielding locally rigid pseudo scene flow labels. We demonstrate the effectiveness of our self-supervised learning method on FlyingThings3D and KITTI datasets. Comprehensive experiments show that our method achieves new state-of-the-art performance in self-supervised scene flow learning, without any ground truth scene flow for supervision, even outperforming some supervised counterparts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RigidFlow_Self-Supervised_Scene_Flow_Learning_on_Point_Clouds_by_Local_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RigidFlow_Self-Supervised_Scene_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RigidFlow_Self-Supervised_Scene_Flow_Learning_on_Point_Clouds_by_Local_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RigidFlow_Self-Supervised_Scene_Flow_Learning_on_Point_Clouds_by_Local_CVPR_2022_paper.html | CVPR 2022 | null |
Smooth Maximum Unit: Smooth Activation Function for Deep Networks Using Smoothing Maximum Technique | Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey | Deep learning researchers have a keen interest in proposing new novel activation functions that can boost neural network performance. A good choice of activation function can have a significant effect on improving network performance and training dynamics. Rectified Linear Unit (ReLU) is a popular hand-designed activation function and is the most common choice in the deep learning community due to its simplicity though ReLU has some drawbacks. In this paper, we have proposed two new novel activation functions based on approximation of the maximum function, and we call these functions Smooth Maximum Unit (SMU and SMU-1). We show that SMU and SMU-1 can smoothly approximate ReLU, Leaky ReLU, or more general Maxout family, and GELU is a particular case of SMU. Replacing ReLU by SMU, Top-1 classification accuracy improves by 6.22%, 3.39%, 3.51%, and 3.08% on the CIFAR100 dataset with ShuffleNet V2, PreActResNet-50, ResNet-50, and SeNet-50 models respectively. Also, our experimental evaluation shows that SMU and SMU-1 improve network performance in a variety of deep learning tasks like image classification, object detection, semantic segmentation, and machine translation compared to widely used activation functions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Biswas_Smooth_Maximum_Unit_Smooth_Activation_Function_for_Deep_Networks_Using_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Biswas_Smooth_Maximum_Unit_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Biswas_Smooth_Maximum_Unit_Smooth_Activation_Function_for_Deep_Networks_Using_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Biswas_Smooth_Maximum_Unit_Smooth_Activation_Function_for_Deep_Networks_Using_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Invisible Markers for Hidden Codes in Offline-to-Online Photography | Jun Jia, Zhongpai Gao, Dandan Zhu, Xiongkuo Min, Guangtao Zhai, Xiaokang Yang | QR (quick response) codes are widely used as an offline-to-online channel to convey information (e.g., links) from publicity materials (e.g., display and print) to mobile devices. However, QR Codes are not favorable for taking up valuable space of publicity materials. Recent works propose invisible codes/hyperlinks that can convey hidden information from offline to online. However, they require markers to locate invisible codes, which fails the purpose of invisible codes to be visible because of the markers. This paper proposes a novel invisible information hiding architecture for display/print-camera scenarios, consisting of hiding, locating, correcting, and recovery, where invisible markers are learned to make hidden codes truly invisible. We hide information in a sub-image rather than the entire image and include a localization module in the end-to-end framework. To achieve both high visual quality and high recovering robustness, an effective multi-stage training strategy is proposed. The experimental results show that the proposed method outperforms the state-of-the-art information hiding methods in both visual quality and robustness. In addition, the automatic localization of hidden codes significantly reduces the time of manually correcting geometric distortions for photos, which is a revolutionary innovation for information hiding in mobile applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jia_Learning_Invisible_Markers_for_Hidden_Codes_in_Offline-to-Online_Photography_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jia_Learning_Invisible_Markers_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_Learning_Invisible_Markers_for_Hidden_Codes_in_Offline-to-Online_Photography_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_Learning_Invisible_Markers_for_Hidden_Codes_in_Offline-to-Online_Photography_CVPR_2022_paper.html | CVPR 2022 | null |
Personalized Image Aesthetics Assessment With Rich Attributes | Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, Yandong Guo | Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image characteristics and subject characters. The existing PIAA databases are limited in terms of annotation diversity, especially the subject aspect, which can no longer meet the increasing demands of PIAA research. To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31,220 images with annotations by 438 subjects. PARA features wealthy annotations, including 9 image-oriented objective attributes and 4 human-oriented subjective attributes. In addition, desensitized subject information, such as personality traits, is also provided to support study of PIAA and user portraits. A comprehensive analysis of the annotation data is provided and statistic study indicates that the aesthetic preferences can be mirrored by proposed subjective attributes. We also propose a conditional PIAA model by utilizing subject information as conditional prior. Experimental results indicate that the conditional PIAA model can outperform the control group, which is also the first attempt to demonstrate how image aesthetics and subject characters interact to produce the intricate personalized tastes on image aesthetics. We believe the database and the associated analysis would be useful for conducting next-generation PIAA study. The project page of PARA can be found at https://cv-datasets.institutecv.com/#/data-sets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Personalized_Image_Aesthetics_Assessment_With_Rich_Attributes_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16754 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Personalized_Image_Aesthetics_Assessment_With_Rich_Attributes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Personalized_Image_Aesthetics_Assessment_With_Rich_Attributes_CVPR_2022_paper.html | CVPR 2022 | null |
Task2Sim: Towards Effective Pre-Training and Transfer From Synthetic Data | Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu (Richard) Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogerio S. Feris | Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost, privacy, usage rights, and ethical issues. In this paper, for the first time, we study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks from very different domains. In using such synthetic data for pre-training, we find that downstream performance on different tasks are favored by different configurations of simulation parameters (e.g. lighting, object pose, backgrounds, etc.), and that there is no one-size-fits-all solution. It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance. We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters to generate synthetic pre-training data for them. Task2Sim learns this mapping by training to find the set of best parameters on a set of "seen" tasks. Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot, without requiring additional training. Given a budget in number of images per class, our extensive experiments with 20 diverse downstream tasks show Task2Sim's task-adaptive pre-training data results in significantly better downstream performance than non-adaptively choosing simulation parameters on both seen and unseen tasks. It is even competitive with pre-training on real images from Imagenet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mishra_Task2Sim_Towards_Effective_Pre-Training_and_Transfer_From_Synthetic_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mishra_Task2Sim_Towards_Effective_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00054 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mishra_Task2Sim_Towards_Effective_Pre-Training_and_Transfer_From_Synthetic_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mishra_Task2Sim_Towards_Effective_Pre-Training_and_Transfer_From_Synthetic_Data_CVPR_2022_paper.html | CVPR 2022 | null |
Part-Based Pseudo Label Refinement for Unsupervised Person Re-Identification | Yoonki Cho, Woo Jae Kim, Seunghoon Hong, Sung-Eui Yoon | Unsupervised person re-identification (re-ID) aims at learning discriminative representations for person retrieval from unlabeled data. Recent techniques accomplish this task by using pseudo-labels, but these labels are inherently noisy and deteriorate the accuracy. To overcome this problem, several pseudo-label refinement methods have been proposed, but they neglect the fine-grained local context essential for person re-ID. In this paper, we propose a novel Part-based Pseudo Label Refinement (PPLR) framework that reduces the label noise by employing the complementary relationship between global and part features. Specifically, we design a cross agreement score as the similarity of k-nearest neighbors between feature spaces to exploit the reliable complementary relationship. Based on the cross agreement, we refine pseudo-labels of global features by ensembling the predictions of part features, which collectively alleviate the noise in global feature clustering. We further refine pseudo-labels of part features by applying label smoothing according to the suitability of given labels for each part. Thanks to the reliable complementary information provided by the cross agreement score, our PPLR effectively reduces the influence of noisy labels and learns discriminative representations with rich local contexts. Extensive experimental results on Market-1501 and MSMT17 demonstrate the effectiveness of the proposed method over the state-of-the-art performance. The code is available at https://github.com/yoonkicho/PPLR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cho_Part-Based_Pseudo_Label_Refinement_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cho_Part-Based_Pseudo_Label_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14675 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cho_Part-Based_Pseudo_Label_Refinement_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cho_Part-Based_Pseudo_Label_Refinement_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation | Yicong Hong, Zun Wang, Qi Wu, Stephen Gould | Most existing works in vision-and-language navigation (VLN) focus on either discrete or continuous environments, training agents that cannot generalize across the two. Although learning to navigate in continuous spaces is closer to the real-world, training such an agent is significantly more difficult than training an agent in discrete spaces. However, recent advances in discrete VLN are challenging to translate to continuous VLN due to the domain gap. The fundamental difference between the two setups is that discrete navigation assumes prior knowledge of the connectivity graph of the environment, so that the agent can effectively transfer the problem of navigation with low-level controls to jumping from node to node with high-level actions by grounding to an image of a navigable direction. To bridge the discrete-to-continuous gap, we propose a predictor to generate a set of candidate waypoints during navigation, so that agents designed with high-level actions can be transferred to and trained in continuous environments. We refine the connectivity graph of Matterport3D to fit the continuous Habitat-Matterport3D, and train the waypoints predictor with the refined graphs to produce accessible waypoints at each time step. Moreover, we demonstrate that the predicted waypoints can be augmented during training to diversify the views and paths, and therefore enhance agent's generalization ability. Through extensive experiments we show that agents navigating in continuous environments with predicted waypoints perform significantly better than agents using low-level actions, which reduces the absolute discrete-to-continuous gap by 11.76% Success Weighted by Path Length (SPL) for the Cross-Modal Matching Agent and 18.24% SPL for the Recurrent VLN-BERT. Our agents, trained with a simple imitation learning objective, outperform previous methods by a large margin, achieving new state-of-the-art results on the testing environments of the R2R-CE and the RxR-CE datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Bridging_the_Gap_Between_Learning_in_Discrete_and_Continuous_Environments_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hong_Bridging_the_Gap_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02764 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Bridging_the_Gap_Between_Learning_in_Discrete_and_Continuous_Environments_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Bridging_the_Gap_Between_Learning_in_Discrete_and_Continuous_Environments_CVPR_2022_paper.html | CVPR 2022 | null |
HDNet: High-Resolution Dual-Domain Learning for Spectral Compressive Imaging | Xiaowan Hu, Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, Luc Van Gool | The rapid development of deep learning provides a better solution for the end-to-end reconstruction of hyperspectral image (HSI). However, existing learning-based methods have two major defects. Firstly, networks with self-attention usually sacrifice internal resolution to balance model performance against complexity, losing fine-grained high-resolution (HR) features. Secondly, even if the optimization focusing on spatial-spectral domain learning (SDL) converges to the ideal solution, there is still a significant visual difference between the reconstructed HSI and the truth. So we propose a high-resolution dual-domain learning network (HDNet) for HSI reconstruction. On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features. On the other hand, frequency domain learning (FDL) is introduced for HSI reconstruction to narrow the frequency domain discrepancy. Dynamic FDL supervision forces the model to reconstruct fine-grained frequencies and compensate for excessive smoothing and distortion caused by pixel-level losses. The HR pixel-level attention and frequency-level refinement in our HDNet mutually promote HSI perceptual quality. Extensive quantitative and qualitative experiments show that our method achieves SOTA performance on simulated and real HSI datasets. https://github.com/Huxiaowan/HDNet | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_HDNet_High-Resolution_Dual-Domain_Learning_for_Spectral_Compressive_Imaging_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.02149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_HDNet_High-Resolution_Dual-Domain_Learning_for_Spectral_Compressive_Imaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_HDNet_High-Resolution_Dual-Domain_Learning_for_Spectral_Compressive_Imaging_CVPR_2022_paper.html | CVPR 2022 | null |
OW-DETR: Open-World Detection Transformer | Akshita Gupta, Sanath Narayan, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah | Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC. Our code is available at https://github.com/akshitac8/OW-DETR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gupta_OW-DETR_Open-World_Detection_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gupta_OW-DETR_Open-World_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_OW-DETR_Open-World_Detection_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_OW-DETR_Open-World_Detection_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Deep Implicit Functions for 3D Shapes With Dynamic Code Clouds | Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, Zhizhong Han | Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape representation. To capture geometry details, current methods usually learn DIF using local latent codes, which discretize the space into a regular 3D grid (or octree) and store local codes in grid points (or octree nodes). Given a query point, the local feature is computed by interpolating its neighboring local codes with their positions. However, the local codes are constrained at discrete and regular positions like grid points, which makes the code positions difficult to be optimized and limits their representation ability. To solve this problem, we propose to learn DIF with Dynamic Code Cloud, named DCC-DIF. Our method explicitly associates local codes with learnable position vectors, and the position vectors are continuous and can be dynamically optimized, which improves the representation ability. In addition, we propose a novel code position loss to optimize the code positions, which heuristically guides more local codes to be distributed around complex geometric details. In contrast to previous methods, our DCC-DIF represents 3D shapes more efficiently with a small amount of local codes, and improves the reconstruction quality. Experiments demonstrate that DCC-DIF achieves better performance over previous methods. Code and data are available at https://github.com/lity20/DCCDIF. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Learning_Deep_Implicit_Functions_for_3D_Shapes_With_Dynamic_Code_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Learning_Deep_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14048 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_Deep_Implicit_Functions_for_3D_Shapes_With_Dynamic_Code_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_Deep_Implicit_Functions_for_3D_Shapes_With_Dynamic_Code_CVPR_2022_paper.html | CVPR 2022 | null |
Reversible Vision Transformers | Karttikeya Mangalam, Haoqi Fan, Yanghao Li, Chao-Yuan Wu, Bo Xiong, Christoph Feichtenhofer, Jitendra Malik | We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory footprint from the depth of the model, Reversible Vision Transformers enable memory efficient scaling of transformer architectures. We adapt two popular models, namely Vision Transformer and Multi-scale Vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible Vision Transformers achieve a reduced memory footprint of up to 15.5x at identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for resource limited training regimes. Finally, we find that the additional computational burden of recomputing activations is more than overcome for deeper models, where throughput can increase up to 3.9x over their non-reversible counterparts. Code and models are available at https://github.com/facebookresearch/mvit. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mangalam_Reversible_Vision_Transformers_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Amodal Panoptic Segmentation | Rohit Mohan, Abhinav Valada | Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation. The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes. To facilitate research on this new task, we extend two established benchmark datasets with pixel-level amodal panoptic segmentation labels that we make publicly available as KITTI-360-APS and BDD100K-APS. We present several strong baselines, along with the amodal panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify the performance in an interpretable manner. Furthermore, we propose the novel amodal panoptic segmentation network (APSNet), as a first step towards addressing this task by explicitly modeling the complex relationships between the occluders and occludes. Extensive experimental evaluations demonstrate that APSNet achieves state-of-the-art performance on both benchmarks and more importantly exemplifies the utility of amodal recognition. The datasets are available at http://amodal-panoptic.cs.uni-freiburg.de | https://openaccess.thecvf.com/content/CVPR2022/papers/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Gravitationally Lensed Black Hole Emission Tomography | Aviad Levis, Pratul P. Srinivasan, Andrew A. Chael, Ren Ng, Katherine L. Bouman | Measurements from the Event Horizon Telescope enabled the visualization of light emission around a black hole for the first time. So far, these measurements have been used to recover a 2D image under the assumption that the emission field is static over the period of acquisition. In this work, we propose BH-NeRF, a novel tomography approach that leverages gravitational lensing to recover the continuous 3D emission field near a black hole. Compared to other 3D reconstruction or tomography settings, this task poses two significant challenges: first, rays near black holes follow curved paths dictated by general relativity, and second, we only observe measurements from a single viewpoint. Our method captures the unknown emission field using a continuous volumetric function parameterized by a coordinate-based neural network, and uses knowledge of Keplerian orbital dynamics to establish correspondence between 3D points over time. Together, these enable BH-NeRF to recover accurate 3D emission fields, even in challenging situations with sparse measurements and uncertain orbital dynamics. This work takes the first steps in showing how future measurements from the Event Horizon Telescope could be used to recover evolving 3D emission around the supermassive black hole in our Galactic center. | https://openaccess.thecvf.com/content/CVPR2022/papers/Levis_Gravitationally_Lensed_Black_Hole_Emission_Tomography_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Levis_Gravitationally_Lensed_Black_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.03715 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Levis_Gravitationally_Lensed_Black_Hole_Emission_Tomography_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Levis_Gravitationally_Lensed_Black_Hole_Emission_Tomography_CVPR_2022_paper.html | CVPR 2022 | null |
3D-Aware Image Synthesis via Learning Structural and Textural Representations | Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, Bolei Zhou | Making generative models 3D-aware bridges the 2D image space and the 3D physical world yet remains challenging. Recent attempts equip a Generative Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D coordinates to pixel values, as a 3D prior. However, the implicit function in NeRF has a very local receptive field, making the generator hard to become aware of the global structure. Meanwhile, NeRF is built on volume rendering which can be too costly to produce high-resolution results, increasing the optimization difficulty. To alleviate these two problems, we propose a novel framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis, through explicitly learning a structural representation and a textural representation. We first learn a feature volume to represent the underlying structure, which is then converted to a feature field using a NeRF-like model. The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis. Such a design enables independent control of the shape and the appearance. Extensive experiments on a wide range of datasets confirm that, our approach achieves sufficiently higher image quality and better 3D control than the previous methods.. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_3D-Aware_Image_Synthesis_via_Learning_Structural_and_Textural_Representations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_3D-Aware_Image_Synthesis_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.10759 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_3D-Aware_Image_Synthesis_via_Learning_Structural_and_Textural_Representations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_3D-Aware_Image_Synthesis_via_Learning_Structural_and_Textural_Representations_CVPR_2022_paper.html | CVPR 2022 | null |
Text-to-Image Synthesis Based on Object-Guided Joint-Decoding Transformer | Fuxiang Wu, Liu Liu, Fusheng Hao, Fengxiang He, Jun Cheng | Object-guided text-to-image synthesis aims to generate images from natural language descriptions built by two-step frameworks, i.e., the model generates the layout and then synthesizes images from the layout and captions. However, such frameworks have two issues: 1) complex structure, since generating language-related layout is not a trivial task; 2) error propagation, because the inappropriate layout will mislead the image synthesis and is hard to be revised. In this paper, we propose an object-guided joint-decoding module to simultaneously generate the image and the corresponding layout. Specially, we present the joint-decoding transformer to model the joint probability on images tokens and the corresponding layouts tokens, where layout tokens provide additional observed data to model the complex scene better. Then, we describe a novel Layout-VQGAN for layout encoding and decoding to provide more information about the complex scene. After that, we present the detail-enhanced module to enrich the language-related details based on two facts: 1) visual details could be omitted in the compression of VQGANs; 2) the joint-decoding transformer would not have sufficient generating capacity. The experiments show that our approach is competitive with previous object-centered models and can generate diverse and high-quality objects under the given layouts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Text-to-Image_Synthesis_Based_on_Object-Guided_Joint-Decoding_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Text-to-Image_Synthesis_Based_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Text-to-Image_Synthesis_Based_on_Object-Guided_Joint-Decoding_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Text-to-Image_Synthesis_Based_on_Object-Guided_Joint-Decoding_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Correlation Verification for Image Retrieval | Seongwon Lee, Hongje Seong, Suhyeon Lee, Euntai Kim | Geometric verification is considered a de facto solution for the re-ranking task in image retrieval. In this study, we propose a novel image retrieval re-ranking network named Correlation Verification Networks (CVNet). Our proposed network, comprising deeply stacked 4D convolutional layers, gradually compresses dense feature correlation into image similarity while learning diverse geometric matching patterns from various image pairs. To enable cross-scale matching, it builds feature pyramids and constructs cross-scale feature correlations within a single inference, replacing costly multi-scale inferences. In addition, we use curriculum learning with the hard negative mining and Hide-and-Seek strategy to handle hard samples without losing generality. Our proposed re-ranking network shows state-of-the-art performance on several retrieval benchmarks with a significant margin (+12.6% in mAP on ROxford-Hard+1M set) over state-of-the-art methods. The source code and models are available online: https://github.com/sungonce/CVNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Correlation_Verification_for_Image_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Correlation_Verification_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01458 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Correlation_Verification_for_Image_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Correlation_Verification_for_Image_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Vision-and-Language Pre-Training via Retrieval-Based Multi-Granular Alignment | Mingyang Zhou, Licheng Yu, Amanpreet Singh, Mengjiao Wang, Zhou Yu, Ning Zhang | Vision-and-Language (V+L) pre-training models have achieved tremendous success in recent years on various multi-modal benchmarks. However, the majority of existing models require pre-training on a large set of parallel image-text data, which is costly to collect, compared to image-only or text-only data. In this paper, we propose unsupervised Vision-and-Language pre-training (UVLP) to learn the cross-modal representation from non-parallel image and text datasets. We found two key factors that lead to good unsupervised V+L pre-training without parallel data: (i) joint image-and-text input (ii) overall image-text alignment (even for non-parallel data). Accordingly, we propose a novel unsupervised V+L pre-training curriculum for non-parallel texts and images. We first construct a weakly aligned image-text corpus via a retrieval-based approach, then apply a set of multi-granular alignment pre-training tasks, including region-to-tag, region-to-phrase, and image-to-sentence alignment, to bridge the gap between the two modalities. A comprehensive ablation study shows each granularity is helpful to learn a stronger pre-trained model. We adapt our pre-trained model to a set of V+L downstream tasks, including VQA, NLVR2, Visual Entailment, and RefCOCO+. Our model achieves the state-of-art performance in all these tasks under the unsupervised setting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Unsupervised_Vision-and-Language_Pre-Training_via_Retrieval-Based_Multi-Granular_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Unsupervised_Vision-and-Language_Pre-Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.00242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Unsupervised_Vision-and-Language_Pre-Training_via_Retrieval-Based_Multi-Granular_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Unsupervised_Vision-and-Language_Pre-Training_via_Retrieval-Based_Multi-Granular_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-Robust Makeup Transfer | Shengshan Hu, Xiaogeng Liu, Yechao Zhang, Minghui Li, Leo Yu Zhang, Hai Jin, Libing Wu | While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks. Recently, some studies adopt adversarial examples to protect photos from being identified by unauthorized face recognition systems. However, existing methods of generating adversarial face images suffer from many limitations, such as awkward visual, white-box setting, weak transferability, making them difficult to be applied to protect face privacy in reality. In this paper, we propose adversarial makeup transfer GAN (AMT-GAN), a novel face protection method aiming at constructing adversarial face images that preserve stronger black-box transferability and better visual quality simultaneously. AMT-GAN leverages generative adversarial networks (GAN) to synthesize adversarial face images with makeup transferred from reference images. In particular, we introduce a new regularization module along with a joint training strategy to reconcile the conflicts between the adversarial noises and the cycle consistence loss in makeup transfer, achieving a desirable balance between the attack strength and visual changes. Extensive experiments verify that compared with state of the arts, AMT-GAN can not only preserve a comfortable visual quality, but also achieve a higher attack success rate over commercial FR APIs, including Face++, Aliyun, and Microsoft. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Protecting_Facial_Privacy_Generating_Adversarial_Identity_Masks_via_Style-Robust_Makeup_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Protecting_Facial_Privacy_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Protecting_Facial_Privacy_Generating_Adversarial_Identity_Masks_via_Style-Robust_Makeup_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Protecting_Facial_Privacy_Generating_Adversarial_Identity_Masks_via_Style-Robust_Makeup_CVPR_2022_paper.html | CVPR 2022 | null |
PONI: Potential Functions for ObjectGoal Navigation With Interaction-Free Learning | Santhosh Kumar Ramakrishnan, Devendra Singh Chaplot, Ziad Al-Halah, Jitendra Malik, Kristen Grauman | State-of-the-art approaches to ObjectGoal navigation (ObjectNav) rely on reinforcement learning and typically require significant computational resources and time for learning. We propose Potential functions for ObjectGoal Navigation with Interaction-free learning (PONI), a modular approach that disentangles the skills of 'where to look?' for an object and 'how to navigate to (x, y)?'. Our key insight is that 'where to look?' can be treated purely as a perception problem, and learned without environment interactions. To address this, we propose a network that predicts two complementary potential functions conditioned on a semantic map and uses them to decide where to look for an unseen object. We train the potential function network using supervised learning on a passive dataset of top-down semantic maps, and integrate it into a modular framework to perform ObjectNav. Experiments on Gibson and Matterport3D demonstrate that our method achieves the state-of-the-art for ObjectNav while incurring up to 1,600x less computational cost for training. Code and pre-trained models are available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ramakrishnan_PONI_Potential_Functions_for_ObjectGoal_Navigation_With_Interaction-Free_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ramakrishnan_PONI_Potential_Functions_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ramakrishnan_PONI_Potential_Functions_for_ObjectGoal_Navigation_With_Interaction-Free_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ramakrishnan_PONI_Potential_Functions_for_ObjectGoal_Navigation_With_Interaction-Free_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning | Jiexi Yan, Lei Luo, Chenghao Xu, Cheng Deng, Heng Huang | How to effectively handle label noise has been one of the most practical but challenging tasks in Deep Neural Networks (DNNs). Recent popular methods for training DNNs with noisy labels mainly focus on directly filtering out samples with low confidence or repeatedly mining valuable information from low-confident samples. %to further modify DNNs. However, they cannot guarantee the robust generalization of models due to the ignorance of useful information hidden in noisy data. To address this issue, we propose a new effective method named as LaCoL (Latent Contrastive Learning) to leverage the negative correlations from the noisy data. Specifically, in label space, we exploit the weakly-augmented data to filter samples and adopt classification loss on strong augmentations of the selected sample set, which can preserve the training diversity. While in metric space, we utilize weakly-supervised contrastive learning to excavate these negative correlations hidden in noisy data. Moreover, a cross-space similarity consistency regularization is provided to constrain the gap between label space and metric space. Extensive experiments have validated the superiority of our approach over existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Noise_Is_Also_Useful_Negative_Correlation-Steered_Latent_Contrastive_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Noise_Is_Also_Useful_Negative_Correlation-Steered_Latent_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Noise_Is_Also_Useful_Negative_Correlation-Steered_Latent_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation | Zhenguang Liu, Runyang Feng, Haoming Chen, Shuang Wu, Yixing Gao, Yunjun Gao, Xiang Wang | Multi-frame human pose estimation has long been a compelling and fundamental problem in computer vision. This task is challenging due to fast motion and pose occlusion that frequently occur in videos. State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame). One aspect that has been obviated so far, is the fact that current methods directly aggregate unaligned contexts across frames. The spatial-misalignment between pose features of the current frame and neighboring frames might lead to unsatisfactory results. More importantly, existing approaches build upon the straightforward pose estimation loss, which unfortunately cannot constrain the network to fully leverage useful information from neighboring frames. To tackle these problems, we present a novel hierarchical alignment framework, which leverages coarse-to-fine deformations to progressively update a neighboring frame to align with the current frame at the feature level. We further propose to explicitly supervise the knowledge extraction from neighboring frames, guaranteeing that useful complementary cues are extracted. To achieve this goal, we theoretically analyzed the mutual information between the frames and arrived at a loss that maximizes the taskrelevant mutual information. These allow us to rank No.1 in the Multi-frame Person Pose Estimation Challenge on benchmark dataset PoseTrack2017, and obtain state-of-the-art performance on benchmarks Sub-JHMDB and PoseTrack2018. Our code is released at https://github.com/Pose-Group/FAMI-Pose, hoping that it will be useful to the community. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Temporal_Feature_Alignment_and_Mutual_Information_Maximization_for_Video-Based_Human_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Temporal_Feature_Alignment_and_Mutual_Information_Maximization_for_Video-Based_Human_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Temporal_Feature_Alignment_and_Mutual_Information_Maximization_for_Video-Based_Human_CVPR_2022_paper.html | CVPR 2022 | null |
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing | Gaurav Parmar, Yijun Li, Jingwan Lu, Richard Zhang, Jun-Yan Zhu, Krishna Kumar Singh | Existing GAN inversion and editing methods work well for aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categories with complex scene layouts and object occlusions, such as cars, animals, and outdoor images. We propose a new method to invert and edit such complex images in the latent space of GANs, such as StyleGAN2. Our key idea is to explore inversion with a collection of layers, spatially adapting the inversion process to the difficulty of the image. We learn to predict the "invertibility" of different image segments and project each segment into a latent layer. Easier regions can be inverted into an earlier layer in the generator's latent space, while more challenging regions can be inverted into a later feature space. Experiments show that our method obtains better inversion results compared to the recent approaches on complex categories, while maintaining downstream editability. Please refer to our project page at gauravparmar.com/sam_ inversion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Parmar_Spatially-Adaptive_Multilayer_Selection_for_GAN_Inversion_and_Editing_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_Spatially-Adaptive_Multilayer_Selection_for_GAN_Inversion_and_Editing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_Spatially-Adaptive_Multilayer_Selection_for_GAN_Inversion_and_Editing_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Transformers for Unsupervised Object Discovery Using Normalized Cut | Yangtao Wang, Xi Shen, Shell Xu Hu, Yuan Yuan, James L. Crowley, Dominique Vaufreydaz | Transformers trained with self-supervision using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this paper, we show a graph-based method that uses the self-supervised transformer features to discover an object from an image. Visual tokens are viewed as nodes in a weighted graph with edges representing a connectivity score based on the similarity of tokens. Foreground objects can then be segmented using a normalized graph-cut to group self-similar regions. We solve the graph-cut problem using spectral clustering with generalized eigen-decomposition and show that the second smallest eigenvector provides a cutting solution since its absolute value indicates the likelihood that a token belongs to a foreground object. Despite its simplicity, this approach significantly boosts the performance of unsupervised object discovery: we improve over the recent state-of-the-art LOST by a margin of 6.9%, 8.1%, and 8.1% respectively on the VOC07, VOC12, and COCO20K. The performance can be further improved by adding a second stage class-agnostic detector (CAD). Our proposed method can be easily extended to unsupervised saliency detection and weakly supervised object detection. For unsupervised saliency detection, we improve IoU for 4.9%, 5.2%, 12.9% on ECSSD, DUTS, DUTOMRON respectively compared to state-of-the-art. For weakly supervised object detection, we achieve competitive performance on CUB and ImageNet. Our code is available at: https://www.m-psi.fr/Papers/TokenCut2022/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Self-Supervised_Transformers_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.11539 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Structure-Aware Transformer Over Interaction Proposals for Human-Object Interaction Detection | Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, Chang-Wen Chen | Recent high-performing Human-Object Interaction (HOI) detection techniques have been highly influenced by Transformer-based object detector (i.e., DETR). Nevertheless, most of them directly map parametric interaction queries into a set of HOI predictions through vanilla Transformer in a one-stage manner. This leaves rich inter- or intra-interaction structure under-exploited. In this work, we design a novel Transformer-style HOI detector, i.e., Structure-aware Transformer over Interaction Proposals (STIP), for HOI detection. Such design decomposes the process of HOI set prediction into two subsequent phases, i.e., an interaction proposal generation is first performed, and then followed by transforming the non-parametric interaction proposals into HOI predictions via a structure-aware Transformer. The structure-aware Transformer upgrades vanilla Transformer by encoding additionally the holistically semantic structure among interaction proposals as well as the locally spatial structure of human/object within each interaction proposal, so as to strengthen HOI predictions. Extensive experiments conducted on V-COCO and HICO-DET benchmarks have demonstrated the effectiveness of STIP, and superior results are reported when comparing with the state-of-the-art HOI detectors. Source code is available at https://github.com/zyong812/STIP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Exploring_Structure-Aware_Transformer_Over_Interaction_Proposals_for_Human-Object_Interaction_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Exploring_Structure-Aware_Transformer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_Structure-Aware_Transformer_Over_Interaction_Proposals_for_Human-Object_Interaction_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_Structure-Aware_Transformer_Over_Interaction_Proposals_for_Human-Object_Interaction_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Robust Adaptive Object Detection Under Noisy Annotations | Xinyu Liu, Wuyang Li, Qiushi Yang, Baopu Li, Yixuan Yuan | Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images. Existing methods assume that the source domain labels are completely clean, yet large-scale datasets often contain error-prone annotations due to instance ambiguity, which may lead to a biased source distribution and severely degrade the performance of the domain adaptive detector de facto. In this paper, we represent the first effort to formulate noisy DAOD and propose a Noise Latent Transferability Exploration (NLTE) framework to address this issue. It is featured with 1) Potential Instance Mining (PIM), which leverages eligible proposals to recapture the miss-annotated instances from the background; 2) Morphable Graph Relation Module (MGRM), which models the adaptation feasibility and transition probability of noisy samples with relation matrices; 3) Entropy-Aware Gradient Reconcilement (EAGR), which incorporates the semantic information into the discrimination process and enforces the gradients provided by noisy and clean samples to be consistent towards learning domain-invariant representations. A thorough evaluation on benchmark DAOD datasets with noisy source annotations validates the effectiveness of NLTE. In particular, NLTE improves the mAP by 8.4% under 60% corrupted annotations and even approaches the ideal upper bound of training on a clean source dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Robust_Adaptive_Object_Detection_Under_Noisy_Annotations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Towards_Robust_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02620 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Robust_Adaptive_Object_Detection_Under_Noisy_Annotations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Robust_Adaptive_Object_Detection_Under_Noisy_Annotations_CVPR_2022_paper.html | CVPR 2022 | null |
Decoupled Multi-Task Learning With Cyclical Self-Regulation for Face Parsing | Qingping Zheng, Jiankang Deng, Zheng Zhu, Ying Li, Stefanos Zafeiriou | This paper probes intrinsic factors behind typical failure cases (e.g spatial inconsistency and boundary confusion) produced by the existing state-of-the-art method in face parsing. To tackle these problems, we propose a novel Decoupled Multi-task Learning with Cyclical Self-Regulation (DML-CSR) for face parsing. Specifically, DML-CSR designs a multi-task model which comprises face parsing, binary edge, and category edge detection. These tasks only share low-level encoder weights without high-level interactions between each other, enabling to decouple auxiliary modules from the whole network at the inference stage. To address spatial inconsistency, we develop a dynamic dual graph convolutional network to capture global contextual information without using any extra pooling operation. To handle boundary confusion in both single and multiple face scenarios, we exploit binary and category edge detection to jointly obtain generic geometric structure and fine-grained semantic clues of human faces. Besides, to prevent noisy labels from degrading model generalization during training, cyclical self-regulation is proposed to self-ensemble several model instances to get a new model and the resulting model then is used to self-distill subsequent models, through alternating iterations. Experiments show that our method achieves the new state-of-the-art performance on the Helen, CelebAMask-HQ, and Lapa datasets. The source code is available at https://github.com/deepinsight/insightface/tree/master/parsing/dml_csr. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Decoupled_Multi-Task_Learning_With_Cyclical_Self-Regulation_for_Face_Parsing_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.14448 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Decoupled_Multi-Task_Learning_With_Cyclical_Self-Regulation_for_Face_Parsing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Decoupled_Multi-Task_Learning_With_Cyclical_Self-Regulation_for_Face_Parsing_CVPR_2022_paper.html | CVPR 2022 | null |
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer | Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change Loy | Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Pastiche_Master_Exemplar-Based_High-Resolution_Portrait_Style_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Pastiche_Master_Exemplar-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Pastiche_Master_Exemplar-Based_High-Resolution_Portrait_Style_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Pastiche_Master_Exemplar-Based_High-Resolution_Portrait_Style_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
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