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Double Low-Rank Representation With Projection Distance Penalty for Clustering | Zhiqiang Fu, Yao Zhao, Dongxia Chang, Xingxing Zhang, Yiming Wang | This paper presents a novel, simple yet robust self-representation method, i.e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the learned optimal projected representations, DLRRPD is capable of obtaining an effective similarity graph to capture the multi-subspace structure. Besides the global low-rank constraint, the local geometrical structure is additionally exploited via a projection distance penalty in our DLRRPD, thus facilitating a more favorable graph. Moreover, to improve the robustness of DLRRPD to noises, we introduce a Laplacian rank constraint, which can further encourage the learned graph to be more discriminative for clustering tasks. Meanwhile, Frobenius norm (instead of the popularly used nuclear norm) is employed to enforce the graph to be more block-diagonal with lower complexity. Extensive experiments have been conducted on synthetic, real, and noisy data to show that the proposed method outperforms currently available alternatives by a margin of 1.0% 10.1%. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Double_Low-Rank_Representation_With_Projection_Distance_Penalty_for_Clustering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Double_Low-Rank_Representation_With_Projection_Distance_Penalty_for_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Double_Low-Rank_Representation_With_Projection_Distance_Penalty_for_Clustering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Double_Low-Rank_Representation_CVPR_2021_supplemental.pdf | null |
Towards High Fidelity Face Relighting With Realistic Shadows | Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu | Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting, allowing us to relight the image while maintaining the local facial details. During training, our model also learns to accurately modify shadows by using estimated shadow masks to emphasize on the high-contrast shadow borders. Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities. With quantitative and qualitative evaluations on the Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows while achieving state-of-the-art face relighting performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00825 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Towards_High_Fidelity_CVPR_2021_supplemental.zip | null |
Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo | Jiahao Lin, Gim Hee Lee | Existing approaches for multi-view multi-person 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views and solve for the 3D pose estimation for each person. Establishing cross-view correspondences is challenging in multi-person scenes, and incorrect correspondences will lead to sub-optimal performance for the multi-stage pipeline. In this work, we present our multi-view 3D pose estimation approach based on plane sweep stereo to jointly address the cross-view fusion and 3D pose reconstruction in a single shot. Specifically, we propose to perform depth regression for each joint of each 2D pose in a target camera view. Cross-view consistency constraints are implicitly enforced by multiple reference camera views via the plane sweep algorithm to facilitate accurate depth regression. We adopt a coarse-to-fine scheme to first regress the person-level depth followed by a per-person joint-level relative depth estimation. 3D poses are obtained from a simple back-projection given the estimated depths. We evaluate our approach on benchmark datasets where it outperforms previous state-of-the-arts while being remarkably efficient. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02273 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.html | CVPR 2021 | null | null |
Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty | Bingbing Zhuang, Manmohan Chandraker | Learning methods for relative camera pose estimation have been developed largely in isolation from classical geometric approaches. The question of how to integrate predictions from deep neural networks (DNNs) and solutions from geometric solvers, such as the 5-point algorithm, has as yet remained under-explored. In this paper, we present a novel framework that involves probabilistic fusion between the two families of predictions during network training, with a view to leveraging their complementary benefits in a learnable way. The fusion is achieved by learning the DNN uncertainty under explicit guidance by the geometric uncertainty, thereby learning to take into account the geometric solution in relation to the DNN prediction. Our network features a self-attention graph neural network, which drives the learning by enforcing strong interactions between different correspondences and potentially modeling complex relationships between points. We propose motion parmeterizations suitable for learning and show that our method achieves state-of-the-art performance on the challenging DeMoN and ScanNet datasets. While we focus on relative pose, we envision that our pipeline is broadly applicable for fusing classical geometry and deep learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhuang_Fusing_the_Old_with_the_New_Learning_Relative_Camera_Pose_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhuang_Fusing_the_Old_with_the_New_Learning_Relative_Camera_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhuang_Fusing_the_Old_with_the_New_Learning_Relative_Camera_Pose_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhuang_Fusing_the_Old_CVPR_2021_supplemental.pdf | null |
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning | Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang | Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo-labeled samples from an unlabeled set, where pseudo-labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at https://github.com/google-research/crest. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_CReST_A_Class-Rebalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.09559 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_CReST_A_Class-Rebalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_CReST_A_Class-Rebalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_CReST_A_Class-Rebalancing_CVPR_2021_supplemental.pdf | null |
Towards Diverse Paragraph Captioning for Untrimmed Videos | Yuqing Song, Shizhe Chen, Qin Jin | Video paragraph captioning aims to describe multiple events in untrimmed videos with descriptive paragraphs. Existing approaches mainly solve the problem in two steps: event detection and then event captioning. Such two-step manner makes the quality of generated paragraphs highly dependent on the accuracy of event proposal detection which is already a challenging task. In this paper, we propose a paragraph captioning model which eschews the problematic event detection stage and directly generates paragraphs for untrimmed videos. To describe coherent and diverse events, we propose to enhance the conventional temporal attention with dynamic video memories, which progressively exposes new video features and suppresses over-accessed video contents to control visual focuses of the model. In addition, a diversity-driven training strategy is proposed to improve diversity of paragraph on the language perspective. Considering that untrimmed videos generally contain massive but redundant frames, we further augment the video encoder with keyframe awareness to improve efficiency. Experimental results on the ActivityNet and Charades datasets show that our proposed model significantly outperforms the state-of-the-art performance on both accuracy and diversity metrics without using any event boundary annotations. Code will be released at https://github.com/syuqings/video-paragraph. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Towards_Diverse_Paragraph_Captioning_for_Untrimmed_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.14477 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Towards_Diverse_Paragraph_Captioning_for_Untrimmed_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Towards_Diverse_Paragraph_Captioning_for_Untrimmed_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Towards_Diverse_Paragraph_CVPR_2021_supplemental.pdf | null |
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation | Yair Kittenplon, Yonina C. Eldar, Dan Raviv | Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow often suffer from poor generalization. Here we present a recurrent architecture that learns a single step of an unrolled iterative alignment procedure for refining scene flow predictions. Inspired by classical algorithms, we demonstrate iterative convergence toward the solution using strong regularization. The proposed method can handle sizeable temporal deformations and suggests a slimmer architecture than competitive all-to-all correlation approaches. Trained on FlyingThings3D synthetic data only, our network successfully generalizes to real scans, outperforming all existing methods by a large margin on the KITTI self-supervised benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kittenplon_FlowStep3D_Model_Unrolling_for_Self-Supervised_Scene_Flow_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10147 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kittenplon_FlowStep3D_Model_Unrolling_for_Self-Supervised_Scene_Flow_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kittenplon_FlowStep3D_Model_Unrolling_for_Self-Supervised_Scene_Flow_Estimation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Adversarial Robustness Across Representation Spaces | Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan | Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial perturbations made to the input pixels. These perturbations are typically measured in an l_p norm. However, robustness often holds only for the specific attack used for training. In this work we extend the above setting to consider the problem of training of deep neural networks that can be made simultaneously robust to perturbations applied in multiple natural representations spaces. For the case of image data, examples include the standard pixel representation as well as the representation in the discrete cosine transform (DCT) basis. We design a theoretically sound algorithm with formal guarantees for the above problem. Furthermore, our guarantees also hold when the goal is to require robustness with respect to multiple l_p norm based attacks. We then derive an efficient practical implementation and demonstrate the effectiveness of our approach on standard datasets for image classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Awasthi_Adversarial_Robustness_Across_Representation_Spaces_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00802 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Awasthi_Adversarial_Robustness_Across_Representation_Spaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Awasthi_Adversarial_Robustness_Across_Representation_Spaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Awasthi_Adversarial_Robustness_Across_CVPR_2021_supplemental.pdf | null |
MagDR: Mask-Guided Detection and Reconstruction for Defending Deepfakes | Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, Bo Zhang | Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide an learnable reconstruction procedure. Adaptive masks are extracted to capture the change in local facial regions. In experiments, MagDR defends three main tasks of deepfakes, and the learned reconstruction pipeline transfers across input data, showing promising performance in defending both black-box and white-box attacks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper.html | CVPR 2021 | null | null |
Neural Deformation Graphs for Globally-Consistent Non-Rigid Reconstruction | Aljaz Bozic, Pablo Palafox, Michael Zollhofer, Justus Thies, Angela Dai, Matthias Niessner | We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation graph via a deep neural network. This neural deformation graph does not rely on any object-specific structure and, thus, can be applied to general non-rigid deformation tracking. Our method globally optimizes this neural graph on a given sequence of depth camera observations of a non-rigidly moving object. Based on explicit viewpoint consistency as well as inter-frame graph and surface consistency constraints, the underlying network is trained in a self-supervised fashion. We additionally optimize for the geometry of the object with an implicit deformable multi-MLP shape representation. Our approach does not assume sequential input data, thus enabling robust tracking of fast motions or even temporally disconnected recordings. Our experiments demonstrate that our Neural Deformation Graphs outperform state-of-the-art non-rigid reconstruction approaches both qualitatively and quantitatively, with 64% improved reconstruction and 54% improved deformation tracking performance. Code is publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bozic_Neural_Deformation_Graphs_for_Globally-Consistent_Non-Rigid_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01451 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bozic_Neural_Deformation_Graphs_for_Globally-Consistent_Non-Rigid_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bozic_Neural_Deformation_Graphs_for_Globally-Consistent_Non-Rigid_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bozic_Neural_Deformation_Graphs_CVPR_2021_supplemental.pdf | null |
Fostering Generalization in Single-View 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors | Jan Bechtold, Maxim Tatarchenko, Volker Fischer, Thomas Brox | Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy of priors at different levels of locality from ground truth input depth maps. We argue that exploiting local priors allows our method to efficiently use input observations, thus improving generalization in visible areas of novel shapes. At the same time, the combination of local and global priors enables meaningful hallucination of unobserved parts resulting in consistent 3D shapes. We show that the hierarchical approach generalizes much better than the global approach. It generalizes not only between different instances of a class but also across classes and to unseen arrangements of objects. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00476 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bechtold_Fostering_Generalization_in_CVPR_2021_supplemental.pdf | null |
Progressive Semantic-Aware Style Transformation for Blind Face Restoration | Chaofeng Chen, Xiaoming Li, Lingbo Yang, Xianhui Lin, Lei Zhang, Kwan-Yee K. Wong | Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images. In this paper, we propose a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration. Specifically, instead of using an encoder-decoder framework as previous methods, we formulate the restoration of LQ face images as a multi-scale progressive restoration procedure through semantic-aware style transformation. Given a pair of LQ face image and its corresponding parsing map, we first generate a multi-scale pyramid of the inputs, and then progressively modulate different scale features from coarse-to-fine in a semantic-aware style transfer way. Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs. In addition, we further introduce a semantic aware style loss which calculates the feature style loss for each semantic region individually to improve the details of face textures. Finally, we pretrain a face parsing network which can generate decent parsing maps from real-world LQ face images. Experiment results show that our model trained with synthetic data can produce more realistic high-resolution results for synthetic LQ inputs than state-of-the-art methods and generalize better to natural LQ face images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.08709 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Progressive_Semantic-Aware_Style_CVPR_2021_supplemental.pdf | null |
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association | Peisong Wen, Qianqian Xu, Yangbangyan Jiang, Zhiyong Yang, Yuan He, Qingming Huang | Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the accuracy of the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable and noisy identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07293 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_Seeking_the_Shape_CVPR_2021_supplemental.zip | null |
Invertible Image Signal Processing | Yazhou Xing, Zian Qian, Qifeng Chen | Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only enables rendering visually appealing sRGB images but also allows recovering nearly perfect RAW data. Due to our framework's inherent reversibility, we can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images without any memory overhead. We also integrate a differentiable JPEG compression simulator that empowers our framework to reconstruct RAW data from JPEG images. Extensive quantitative and qualitative experiments on two DSLR demonstrate that our method obtains much higher quality in both rendered sRGB images and reconstructed RAW data than alternative methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xing_Invertible_Image_Signal_Processing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15061 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xing_Invertible_Image_Signal_Processing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xing_Invertible_Image_Signal_Processing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xing_Invertible_Image_Signal_CVPR_2021_supplemental.pdf | null |
Lighting, Reflectance and Geometry Estimation From 360deg Panoramic Stereo | Junxuan Li, Hongdong Li, Yasuyuki Matsushita | We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360deg stereo images. Our model takes advantage of the 360deg input to observe the entire scene with geometric detail, then jointly estimates the scene's properties with physical constraints. We first reconstruct a near-field environment light for predicting the lighting at any 3D location within the scene. Then we present a deep learning model that leverages the stereo information to infer the reflectance and surface normal. Lastly, we incorporate the physical constraints between lighting and geometry to refine the reflectance of the scene. Both quantitative and qualitative experiments show that our method, benefiting from the 360deg observation of the scene, outperforms prior state-of-the-art methods and enables more augmented reality applications such as mirror-objects insertion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Lighting_Reflectance_and_Geometry_Estimation_From_360deg_Panoramic_Stereo_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Lighting_Reflectance_and_Geometry_Estimation_From_360deg_Panoramic_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Lighting_Reflectance_and_Geometry_Estimation_From_360deg_Panoramic_Stereo_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Lighting_Reflectance_and_CVPR_2021_supplemental.zip | null |
Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation | Dohun Lim, Hyeonseok Lee, Sungchan Kim | We present a novel method for reliably explaining the predictions of neural networks. We consider an explanation reliable if it identifies input features relevant to the model output by considering the input and the neighboring data points. Our method is built on top of the assumption of smooth landscape in a loss function of the model prediction: locally consistent loss and gradient profile. A theoretical analysis established in this study suggests that those locally smooth model explanations are learned using a batch of noisy copies of the input with the L1 regularization for a saliency map. Extensive experiments support the analysis results, revealing that the proposed saliency maps retrieve the original classes of adversarial examples crafted against both naturally and adversarially trained models, significantly outperforming previous methods. We further demonstrated that such good performance results from the learning capability of this method to identify input features that are truly relevant to the model output of the input and the neighboring data points, fulfilling the requirements of a reliable explanation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lim_Building_Reliable_Explanations_of_Unreliable_Neural_Networks_Locally_Smoothing_Perspective_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lim_Building_Reliable_Explanations_of_Unreliable_Neural_Networks_Locally_Smoothing_Perspective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lim_Building_Reliable_Explanations_of_Unreliable_Neural_Networks_Locally_Smoothing_Perspective_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lim_Building_Reliable_Explanations_CVPR_2021_supplemental.pdf | null |
NeX: Real-Time View Synthesis With Neural Basis Expansion | Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, Supasorn Suwajanakorn | We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects--in real time. Unlike traditional MPI that uses a set of simple RGBa planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forward-facing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as the rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000x faster rendering time than the state of the art. For real-time demos, visit https://nex-mpi.github.io/ | https://openaccess.thecvf.com/content/CVPR2021/papers/Wizadwongsa_NeX_Real-Time_View_Synthesis_With_Neural_Basis_Expansion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05606 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wizadwongsa_NeX_Real-Time_View_Synthesis_With_Neural_Basis_Expansion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wizadwongsa_NeX_Real-Time_View_Synthesis_With_Neural_Basis_Expansion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wizadwongsa_NeX_Real-Time_View_CVPR_2021_supplemental.pdf | null |
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions | Yuntao Qu, Shasha Mo, Jianwei Niu | In real application scenarios, the performance of deep networks may be degraded when the dataset contains noisy labels. Existing methods for learning with noisy labels are limited by two aspects. Firstly, methods based on the noise probability modeling can only be applied to class-level noisy labels. Secondly, others based on the memorization effect outperform in synthetic noise but get weak promotion in real-world noisy datasets. To solve these problems, this paper proposes a novel label-noise robust method named Discrepant Adversarial Training (DAT). The DAT method has ability of enforcing prominent feature extraction by matching feature distribution between clean and noisy data. Therefore, under the noise-free feature representation, the deep network can simply output the correct result. To better capture the divergence between the noisy and clean distribution, a new metric is designed to change the distribution divergence into computable. By minimizing the proposed metric with a min-max training of discrepancy on classifiers and generators, DAT can match noisy data to clean data in the feature space. To the best of our knowledge, DAT is the first to address the noisy label problem from the perspective of the feature distribution. Experiments on synthetic and real-world noisy datasets demonstrate that DAT can consistently outperform other state-of-the-art methods. Codes are available at https://github.com/Tyqnn0323/DAT. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qu_DAT_Training_Deep_Networks_Robust_To_Label-Noise_by_Matching_the_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qu_DAT_Training_Deep_Networks_Robust_To_Label-Noise_by_Matching_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qu_DAT_Training_Deep_Networks_Robust_To_Label-Noise_by_Matching_the_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qu_DAT_Training_Deep_CVPR_2021_supplemental.pdf | null |
Repetitive Activity Counting by Sight and Sound | Yunhua Zhang, Ling Shao, Cees G. M. Snoek | This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the corresponding sound into the repetition counting process. This benefits accuracy in challenging vision conditions such as occlusion, dramatic camera view changes, low resolution, etc. We propose a model that starts with analyzing the sight and sound streams separately. Then an audiovisual temporal stride decision module and a reliability estimation module are introduced to exploit cross-modal temporal interaction. For learning and evaluation, an existing dataset is repurposed and reorganized to allow for repetition counting with sight and sound. We also introduce a variant of this dataset for repetition counting under challenging vision conditions. Experiments demonstrate the benefit of sound, as well as the other introduced modules, for repetition counting. Our sight-only model already outperforms the state-of-the-art by itself, when we add sound, results improve notably, especially under harsh vision conditions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Repetitive_Activity_Counting_by_Sight_and_Sound_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Repetitive_Activity_Counting_by_Sight_and_Sound_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Repetitive_Activity_Counting_by_Sight_and_Sound_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Repetitive_Activity_Counting_CVPR_2021_supplemental.zip | null |
PointGuard: Provably Robust 3D Point Cloud Classification | Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong | 3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to adversarial attacks. In particular, an attacker can make a classifier predict an incorrect label for a 3D point cloud via carefully modifying, adding, and/or deleting a small number of its points. Randomized smoothing is state-of-the-art technique to build certifiably robust 2D image classifiers. However, when applied to 3D point cloud classification, randomized smoothing can only certify robustness against adversarially modified points. In this work, we propose PointGuard, the first defense that has provable robustness guarantees against adversarially modified, added, and/or deleted points. Specifically, given a 3D point cloud and an arbitrary point cloud classifier, our PointGuard first creates multiple subsampled point clouds, each of which contains a random subset of the points in the original point cloud; then our PointGuard predicts the label of the original point cloud as the majority vote among the labels of the subsampled point clouds predicted by the point cloud classifier. Our first major theoretical contribution is that we show PointGuard provably predicts the same label for a 3D point cloud when the number of adversarially modified, added, and/or deleted points is bounded. Our second major theoretical contribution is that we prove the tightness of our derived bound when no assumptions on the point cloud classifier are made. Moreover, we design an efficient algorithm to compute our certified robustness guarantees. We also empirically evaluate PointGuard on ModelNet40 and ScanNet benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_PointGuard_Provably_Robust_3D_Point_Cloud_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PointGuard_Provably_Robust_3D_Point_Cloud_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PointGuard_Provably_Robust_3D_Point_Cloud_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_PointGuard_Provably_Robust_CVPR_2021_supplemental.pdf | null |
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification | Zechen Bai, Zhigang Wang, Jian Wang, Di Hu, Errui Ding | Unsupervised domain adaptation (UDA) methods for person re-identification (re-ID) aim at transferring re-ID knowledge from labeled source data to unlabeled target data. Among these methods, the pseudo-label-based branch has achieved great success, whereas most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited. To make full use of the valuable labeled data, we introduce the multi-source concept into UDA person re-ID field, where multiple source datasets are used during training. However, because of domain gaps, simply combining different datasets only brings limited improvement. In this paper, we try to address this problem from two perspectives, i.e. domain-specific view and domain-fusion view. Two constructive modules are proposed, and they are compatible with each other. First, a rectification domain-specific batch normalization (RDSBN) module is explored to simultaneously reduce domain-specific characteristics and increase the distinctiveness of person features. Second, a graph convolutional network (GCN) based multi-domain information fusion (MDIF) module is developed, which minimizes domain distances by fusing features of different domains. The proposed method outperforms state-of-the-art UDA person re-ID methods by a large margin, and even achieves comparable performance to the supervised approaches without any post-processing techniques. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Unsupervised_Multi-Source_Domain_Adaptation_for_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.12961 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Unsupervised_Multi-Source_Domain_Adaptation_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Unsupervised_Multi-Source_Domain_Adaptation_for_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_Unsupervised_Multi-Source_Domain_CVPR_2021_supplemental.pdf | null |
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation | Jungbeom Lee, Jihun Yi, Chaehun Shin, Sungroh Yoon | Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level information from the behavior of a trained object detector, by seeking the smallest areas of the image from which the object detector produces almost the same result as it does from the whole image. These areas constitute a bounding-box attribution map (BBAM), which identifies the target object in its bounding box and thus serves as pseudo ground-truth for weakly supervised semantic and instance segmentation. This approach significantly outperforms recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in weakly supervised semantic and instance segmentation. In addition, we provide a detailed analysis of our method, offering deeper insight into the behavior of the BBAM. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08907 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_BBAM_Bounding_Box_CVPR_2021_supplemental.pdf | null |
Boosting Video Representation Learning With Multi-Faceted Integration | Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Xiao-Ping Zhang, Dong Wu, Tao Mei | Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the problem as visual-semantic embedding learning, which explicitly maps video representation into a rich semantic embedding space, and jointly optimizes video representation from two perspectives. One is to capitalize on the intra-facet supervision between each video and its own label descriptions, and the second predicts the "semantic representation" of each video from the facets of other datasets as the inter-facet supervision. Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The pre-learnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Boosting_Video_Representation_Learning_With_Multi-Faceted_Integration_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Boosting_Video_Representation_Learning_With_Multi-Faceted_Integration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Boosting_Video_Representation_Learning_With_Multi-Faceted_Integration_CVPR_2021_paper.html | CVPR 2021 | null | null |
Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection | Zonghao Guo, Chang Liu, Xiaosong Zhang, Jianbin Jiao, Xiangyang Ji, Qixiang Ye | Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely packed object layouts. CFA is rooted in convex-hull feature representation, which defines a set of dynamically predicted feature points guided by the convex intersection over union (CIoU) to bound the extent of objects. CFA pursues optimal feature assignment by constructing convex-hull sets and dynamically splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA alleviates spatial feature aliasing towards optimal feature adaptation. Experiments on DOTA and SKU110K-R datasets show that CFA significantly outperforms the baseline approach, achieving new state-of-the-art detection performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.html | CVPR 2021 | null | null |
3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management | Tianyi Zhao, Kai Cao, Jiawen Yao, Isabella Nogues, Le Lu, Lingyun Huang, Jing Xiao, Zhaozheng Yin, Ling Zhang | The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20, 8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach. We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segmentation-mesh-classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients' CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_3D_Graph_Anatomy_Geometry-Integrated_Network_for_Pancreatic_Mass_Segmentation_Diagnosis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04701 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_3D_Graph_Anatomy_Geometry-Integrated_Network_for_Pancreatic_Mass_Segmentation_Diagnosis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_3D_Graph_Anatomy_Geometry-Integrated_Network_for_Pancreatic_Mass_Segmentation_Diagnosis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks | Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang | Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ong_Protecting_Intellectual_Property_of_Generative_Adversarial_Networks_From_Ambiguity_Attacks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.04362 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ong_Protecting_Intellectual_Property_of_Generative_Adversarial_Networks_From_Ambiguity_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ong_Protecting_Intellectual_Property_of_Generative_Adversarial_Networks_From_Ambiguity_Attacks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ong_Protecting_Intellectual_Property_CVPR_2021_supplemental.pdf | null |
End-to-End High Dynamic Range Camera Pipeline Optimization | Nicolas Robidoux, Luis E. Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, Felix Heide | With a 280 dB dynamic range, the real world is a High Dynamic Range (HDR) world. Today's sensors cannot record this dynamic range in a single shot. Instead, HDR cameras acquire multiple measurements with different exposures, gains and photodiodes, from which an Image Signal Processor (ISP) reconstructs an HDR image. HDR image recovery for dynamic scenes is an open challenge because of motion and because stitched captures have different noise characteristics, resulting in artefacts that the ISP has to resolve---in real time and at triple-digit megapixel resolutions. Traditionally, hardware ISP settings used by downstream vision modules have been chosen by domain experts. Such frozen camera designs are then used for training data acquisition and supervised learning of downstream vision modules. We depart from this paradigm and formulate HDR ISP hyperparameter search as an end-to-end optimization problem. We propose a mixed 0th and 1st-order block coordinate descent optimizer to jointly learn ISP and detector network weights using RAW image data augmented with emulated SNR transition region artefacts. We assess the proposed method for human vision and image understanding. For automotive object detection, the method improves mAP and mAR by 33% compared to expert-tuning and by 22% compared to recent state-of-the-art. The method is validated in an HDR laboratory rig and in the field, outperforming conventional handcrafted HDR imaging and vision pipelines in all experiments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Robidoux_End-to-End_High_Dynamic_Range_Camera_Pipeline_Optimization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Robidoux_End-to-End_High_Dynamic_Range_Camera_Pipeline_Optimization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Robidoux_End-to-End_High_Dynamic_Range_Camera_Pipeline_Optimization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Robidoux_End-to-End_High_Dynamic_CVPR_2021_supplemental.pdf | null |
Parser-Free Virtual Try-On via Distilling Appearance Flows | Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, Ping Luo | Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results would lead to unrealistic try-on images with large artifacts. Inaccurate parsing misleads parser-based methods to produce visually unrealistic results where artifacts usually occur. A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model. However, the image quality of the student is bounded by the parser-based model. To address this problem, we propose a novel approach, "teacher-tutor-student" knowledge distillation, which is able to produce highly photo-realistic images without human parsing, possessing several appealing advantages compared to prior arts. (1) Unlike existing work, our approach treats the fake images produced by the parser-based method as "tutor knowledge", where the artifacts can be corrected by real "teacher knowledge", which is extracted from the real person images in a self-supervised way. (2) Other than using real images as supervisions, we formulate knowledge distillation in the try-on problem as distilling the appearance flows between the person image and the garment image, enabling us to find accurate dense correspondences between them to produce high-quality results. (3) Extensive evaluations show large superiority of our method (see Fig. 1). | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04559 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_Parser-Free_Virtual_Try-On_CVPR_2021_supplemental.pdf | null |
GIRAFFE: Representing Scenes As Compositional Generative Neural Feature Fields | Michael Niemeyer, Andreas Geiger | Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional. Further, only few works consider the compositional nature of scenes. Our key hypothesis is that incorporating a compositional 3D scene representation into the generative model leads to more controllable image synthesis. Representing scenes as compositional generative neural feature fields allows us to disentangle one or multiple objects from the background as well as individual objects' shapes and appearances while learning from unstructured and unposed image collections without any additional supervision. Combining this scene representation with a neural rendering pipeline yields a fast and realistic image synthesis model. As evidenced by our experiments, our model is able to disentangle individual objects and allows for translating and rotating them in the scene as well as changing the camera pose. | https://openaccess.thecvf.com/content/CVPR2021/papers/Niemeyer_GIRAFFE_Representing_Scenes_As_Compositional_Generative_Neural_Feature_Fields_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12100 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Niemeyer_GIRAFFE_Representing_Scenes_As_Compositional_Generative_Neural_Feature_Fields_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Niemeyer_GIRAFFE_Representing_Scenes_As_Compositional_Generative_Neural_Feature_Fields_CVPR_2021_paper.html | CVPR 2021 | null | null |
Single-Stage Instance Shadow Detection With Bidirectional Relation Learning | Tianyu Wang, Xiaowei Hu, Chi-Wing Fu, Pheng-Ann Heng | Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to form the final shadow-object pairs. In this paper, we present a new single-stage fully-convolutional network architecture with a bidirectional relation learning module to directly learn the relations of shadow and object instances in an end-to-end manner. Compared with the prior work, our method actively explores the internal relationship between shadows and objects to learn a better pairing between them, thus improving the overall performance for instance shadow detection. We evaluate our method on the benchmark dataset for instance shadow detection, both quantitatively and visually. The experimental results demonstrate that our method clearly outperforms the state-of-the-art method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Single-Stage_Instance_Shadow_CVPR_2021_supplemental.pdf | null |
High-Speed Image Reconstruction Through Short-Term Plasticity for Spiking Cameras | Yajing Zheng, Lingxiao Zheng, Zhaofei Yu, Boxin Shi, Yonghong Tian, Tiejun Huang | Fovea, located in the centre of the retina, is specialized for high-acuity vision. Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40,000 Hz, and outputs asynchronous binary spike streams. Although the temporal resolution of visual information is improved, how to reconstruct the scenes is still a challenging problem. In this paper, we present a novel high-speed image reconstruction model through the short-term plasticity (STP) mechanism of the brain. We derive the relationship between postsynaptic potential regulated by STP and the firing frequency of each pixel. By setting up the STP model at each pixel of the spiking camera, we can infer the scene radiance with the temporal regularity of the spike stream. Moreover, we show that STP can be used to distinguish the static and motion areas and further enhance the reconstruction results. The experimental results show that our methods achieve state-of-the-art performance in both image quality and computing time. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_High-Speed_Image_Reconstruction_Through_Short-Term_Plasticity_for_Spiking_Cameras_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_High-Speed_Image_Reconstruction_Through_Short-Term_Plasticity_for_Spiking_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_High-Speed_Image_Reconstruction_Through_Short-Term_Plasticity_for_Spiking_Cameras_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_High-Speed_Image_Reconstruction_CVPR_2021_supplemental.zip | null |
Self-Supervised 3D Mesh Reconstruction From Single Images | Tao Hu, Liwei Wang, Xiaogang Xu, Shu Liu, Jiaya Jia | Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation. However, without explicit 3D attribute-level supervision, it is still difficult to achieve satisfying reconstruction accuracy. In this paper, we propose a Self-supervised Mesh Reconstruction (SMR) approach to enhance 3D mesh attribute learning process. Our approach is motivated by observations that (1) 3D attributes from interpolation and prediction should be consistent, and (2) feature representation of landmarks from all images should be consistent. By only requiring silhouette mask annotation, our SMR can be trained in an end-to-end manner and generalizes to reconstruct natural objects of birds, cows, motorbikes, etc. Experiments demonstrate that our approach improves both 2D supervised and unsupervised 3D mesh reconstruction on multiple datasets. We also show that our model can be adapted to other image synthesis tasks, e.g., novel view generation, shape transfer, and texture transfer, with promising results. Our code is publicly available at https://github.com/Jia-Research-Lab. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Self-Supervised_3D_Mesh_CVPR_2021_supplemental.pdf | null |
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement | Hao Lu, Hu Han, S. Kevin Zhou | Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc. Existing methods mainly focus on how to enhance or extract the very weak blood volume pulse (BVP) signals from face videos, but seldom explicitly model the noises that dominate face video content. Thus, they may suffer from poor generalization ability in unseen scenarios. This paper proposes a novel adversarial learning approach for rPPG based physiological measurement by using Dual Generative Adversarial Networks (Dual-GAN) to model the BVP estimation and noise distribution jointly. The BVP-GAN aims to learn a noise-resistant mapping from input to ground-truth BVP, and the Noise-GAN aims to learn the noise distribution. The dual GANs can promote each other's capability, leading to improved feature disentanglement between BVP and noises. Besides, a plug-and-play block named ROI alignment and fusion (ROI-AF) block is proposed to alleviate the inconsistencies between different ROIs and exploit informative features from a wider receptive field in terms of ROIs. In comparison to state-of-the-art methods, our method achieves better performance in heart rate, heart rate variability, and respiration frequency estimation from face videos. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.html | CVPR 2021 | null | null |
Audio-Visual Instance Discrimination with Cross-Modal Agreement | Pedro Morgado, Nuno Vasconcelos, Ishan Misra | We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show that optimizing for cross-modal discrimination, rather than within-modal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves highly competitive performance when finetuned on action recognition tasks. Furthermore, while recent work in contrastive learning defines positive and negative samples as individual instances, we generalize this definition by exploring cross-modal agreement. We group together multiple instances as positives by measuring their similarity in both the video and audio feature spaces. Cross-modal agreement creates better positive and negative sets, which allows us to calibrate visual similarities by seeking within-modal discrimination of positive instances, and achieve significant gains on downstream tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Morgado_Audio-Visual_Instance_Discrimination_with_Cross-Modal_Agreement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.12943 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Morgado_Audio-Visual_Instance_Discrimination_with_Cross-Modal_Agreement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Morgado_Audio-Visual_Instance_Discrimination_with_Cross-Modal_Agreement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morgado_Audio-Visual_Instance_Discrimination_CVPR_2021_supplemental.pdf | null |
Combined Depth Space Based Architecture Search for Person Re-Identification | Hanjun Li, Gaojie Wu, Wei-Shi Zheng | Most works on person re-identification (ReID) take advantage of large backbone networks such as ResNet, which are designed for image classification instead of ReID, for feature extraction. However, these backbones may not be computationally efficient or the most suitable architectures for ReID. In this work, we aim to design a lightweight and suitable network for ReID. To this end, we propose a novel search space called Combined Depth Space (CDS), based on which we search for an efficient network architecture, which we call CDNet, via a differentiable architecture search algorithm. Through the use of the combined basic building blocks in CDS, CDNet tends to focus on combined pattern information that is typically found in images of pedestrians. We then propose a low-cost search strategy named the Top-k Sample Search strategy to make full use of the search space and avoid trapping in local optimal result. Furthermore, an effective Fine-grained Balance Neck (FBLNeck), which is removable at the inference time, is presented to balance the effects of triplet loss and softmax loss during the training process. Extensive experiments show that our CDNet ( 1.8 M parameters) has comparable performance with state-of-the-art lightweight networks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Combined_Depth_Space_Based_Architecture_Search_for_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04163 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Combined_Depth_Space_Based_Architecture_Search_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Combined_Depth_Space_Based_Architecture_Search_for_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Combined_Depth_Space_CVPR_2021_supplemental.pdf | null |
Rethinking BiSeNet for Real-Time Semantic Segmentation | Mingyuan Fan, Shenqi Lai, Junshi Huang, Xiaoming Wei, Zhenhua Chai, Junfeng Luo, Xiaolin Wei | BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However, its principle of adding an extra path to encode spatial information is time-consuming, and the backbones borrowed from pretrained tasks, e.g., image classification, may be inefficient for image segmentation due to the deficiency of task-specific design. To handle these problems, we propose a novel and efficient structure named Short-Term Dense Concatenate network (STDC network) by removing structure redundancy. Specifically, we gradually reduce the dimension of feature maps and use the aggregation of them for image representation, which forms the basic module of STDC network. In the decoder, we propose a Detail Aggregation module by integrating the learning of spatial information into low-level layers in single-stream manner. Finally, the low-level features and deep features are fused to predict the final segmentation results. Extensive experiments on Cityscapes and CamVid dataset demonstrate the effectiveness of our method by achieving promising trade-off between segmentation accuracy and inference speed. On Cityscapes, we achieve 71.9% mIoU on the test set with a speed of 250.4 FPS on NVIDIA GTX 1080Ti, which is 45.2% faster than the latest methods, and achieve 76.8% mIoU with 97.0 FPS while inferring on higher resolution images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.13188 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
The Spatially-Correlative Loss for Various Image Translation Tasks | Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai | We propose a novel spatially-correlative loss that is simple, efficient, and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired image-to-image (I2I) translation. Previous methods attempt this by using pixel-level cycle-consistency or feature-level matching losses, but the domain-specific nature of these losses hinder translation across large domain gaps. To address this, we exploit the spatial patterns of self-similarity as a means of defining scene structure. Our spatially-correlative loss is geared towards only capturing spatial relationships within an image rather than domain appearance. We also introduce a new self-supervised learning method to explicitly learn spatially-correlative maps for each specific translation task. We show distinct improvement over baseline models in all three modes of unpaired I2I translation: single-modal, multi-modal, and even single-image translation. This new loss can easily be integrated into existing network architectures and thus allows wide applicability. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_The_Spatially-Correlative_Loss_for_Various_Image_Translation_Tasks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00854 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_The_Spatially-Correlative_Loss_for_Various_Image_Translation_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_The_Spatially-Correlative_Loss_for_Various_Image_Translation_Tasks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning To Restore Hazy Video: A New Real-World Dataset and a New Method | Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Fei Wang | Most of the existing deep learning-based dehazing methods are trained and evaluated on the image dehazing datasets, where the dehazed images are generated by only exploiting the information from the corresponding hazy ones. On the other hand, the video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets. Therefore, we propose the first REal-world VIdeo DEhazing (REVIDE) dataset which can be used for the supervised learning of the video dehazing algorithms. By utilizing a well-designed video acquisition system, we can capture paired real-world hazy and haze-free videos that are perfectly aligned by recording the same scene (with or without haze) twice. Considering the challenge of exploiting temporal redundancy among the hazy frames, we also develop a Confidence Guided and Improved Deformable Network (CG-IDN) for video dehazing. The experiments demonstrate that the hazy scenes in the REVIDE dataset are more realistic than the synthetic datasets and the proposed algorithm also performs favorably against state-of-the-art dehazing methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_To_Restore_Hazy_Video_A_New_Real-World_Dataset_and_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_To_Restore_Hazy_Video_A_New_Real-World_Dataset_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_To_Restore_Hazy_Video_A_New_Real-World_Dataset_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Learning_To_Restore_CVPR_2021_supplemental.pdf | null |
DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking | Kha Gia Quach, Pha Nguyen, Huu Le, Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Khoa Luu | Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.06856 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Quach_DyGLIP_A_Dynamic_CVPR_2021_supplemental.zip | https://openaccess.thecvf.com |
Towards Efficient Tensor Decomposition-Based DNN Model Compression With Optimization Framework | Miao Yin, Yang Sui, Siyu Liao, Bo Yuan | Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neural networks (RNNs). However, compressing convolutional neural networks (CNNs) using TT/TR always suffers significant accuracy loss. In this paper, we propose a systematic framework for tensor decomposition-based model compression using Alternating Direction Method of Multipliers (ADMM). By formulating TT decomposition-based model compression to an optimization problem with constraints on tensor ranks, we leverage ADMM technique to systemically solve this optimization problem in an iterative way. During this procedure, the entire DNN model is trained in the original structure instead of TT format, but gradually enjoys the desired low tensor rank characteristics. We then decompose this uncompressed model to TT format and fine-tune it to finally obtain a high-accuracy TT-format DNN model. Our framework is very general, and it works for both CNNs and RNNs, and can be easily modified to fit other tensor decomposition approaches. We evaluate our proposed framework on different DNN models for image classification and video recognition tasks. Experimental results show that our ADMM-based TT-format models demonstrate very high compression performance with high accuracy. Notably, on CIFAR-100, with 2.3X and 2.4X compression ratios, our models have 1.96% and 2.21% higher top-1 accuracy than the original ResNet-20 and ResNet-32, respectively. For compressing ResNet-18 on ImageNet, our model achieves 2.47X FLOPs reduction without accuracy loss. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Towards_Efficient_Tensor_Decomposition-Based_DNN_Model_Compression_With_Optimization_Framework_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Towards_Efficient_Tensor_Decomposition-Based_DNN_Model_Compression_With_Optimization_Framework_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Towards_Efficient_Tensor_Decomposition-Based_DNN_Model_Compression_With_Optimization_Framework_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Towards_Efficient_Tensor_CVPR_2021_supplemental.pdf | null |
User-Guided Line Art Flat Filling With Split Filling Mechanism | Lvmin Zhang, Chengze Li, Edgar Simo-Serra, Yi Ji, Tien-Tsin Wong, Chunping Liu | Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_User-Guided_Line_Art_Flat_Filling_With_Split_Filling_Mechanism_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_User-Guided_Line_Art_Flat_Filling_With_Split_Filling_Mechanism_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_User-Guided_Line_Art_Flat_Filling_With_Split_Filling_Mechanism_CVPR_2021_paper.html | CVPR 2021 | null | null |
Restore From Restored: Video Restoration With Pseudo Clean Video | Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim | In this study, we propose a self-supervised video denoising method called ""restore-from-restored."" This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional neural network (FCN) as the baseline, we can improve video denoising performance without accurate optical flow estimation and registration steps, in contrast to many conventional video restoration methods, due to the translation equivariant property of the FCN. Specifically, the proposed method can take advantage of plentiful similar patches existing across multiple consecutive frames (i.e., patch-recurrence); these patches can boost the performance of the baseline network by a large margin. We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames. In our experiments, we apply the proposed method to state-of-the-art denoisers and show that our fine-tuned networks achieve a considerable improvement in denoising performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.04279 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.html | CVPR 2021 | null | null |
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion | Shi Qiu, Saeed Anwar, Nick Barnes | Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. In this work, we concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. On the one hand, to reduce the ambiguity in nearby points, we augment their local context by fully utilizing both geometric and semantic features in a bilateral structure. On the other hand, we comprehensively interpret the distinctness of the points from multiple resolutions and represent the feature map following an adaptive fusion method at point-level for accurate semantic segmentation. Further, we provide specific ablation studies and intuitive visualizations to validate our key modules. By comparing with state-of-the-art networks on three different benchmarks, we demonstrate the effectiveness of our network. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07074 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiu_Semantic_Segmentation_for_CVPR_2021_supplemental.pdf | null |
Interactive Self-Training With Mean Teachers for Semi-Supervised Object Detection | Qize Yang, Xihan Wei, Biao Wang, Xian-Sheng Hua, Lei Zhang | The goal of semi-supervised object detection is to learn a detection model using only a few labeled data and large amounts of unlabeled data, thereby reducing the cost of data labeling. Although a few studies have proposed various self-training-based methods or consistency regularization-based methods, they ignore the discrepancies among the detection results in the same image that occur during different training iterations. Additionally, the predicted detection results vary among different detection models. In this paper, we propose an interactive form of self-training using mean teachers for semi-supervised object detection. Specifically, to alleviate the instability among the detection results in different iterations, we propose using nonmaximum suppression to fuse the detection results from different iterations. Simultaneously, we use multiple detection heads that predict pseudo labels for each other to provide complementary information. Furthermore, to avoid different detection heads collapsing to each other, we use a mean teacher model instead of the original detection model to predict the pseudo labels. Thus, the object detection model can be trained on both labeled and unlabeled data. Extensive experimental results verify the effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Interactive_Self-Training_With_Mean_Teachers_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Interactive_Self-Training_With_Mean_Teachers_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Interactive_Self-Training_With_Mean_Teachers_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
DeFLOCNet: Deep Image Editing via Flexible Low-Level Controls | Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han, Jing Liao, Bin Jiang, Wei Liu | User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse lowlevel inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (i.e., free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which is based on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks indicate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12723 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_DeFLOCNet_Deep_Image_CVPR_2021_supplemental.pdf | null |
Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs | Xudong Lin, Gedas Bertasius, Jue Wang, Shih-Fu Chang, Devi Parikh, Lorenzo Torresani | We present Vx2Text, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, which have been shown to be effective at modeling language, each modality is first converted into a set of language embeddings by a learnable tokenizer. This allows our approach to perform multimodal fusion in the language space, thus eliminating the need for ad-hoc cross-modal fusion modules. To address the non-differentiability of tokenization on continuous inputs (e.g., video or audio), we utilize a relaxation scheme that enables end-to-end training. Furthermore, unlike prior encoder-only models, our network includes an autoregressive decoder to generate open-ended text from the multimodal embeddings fused by the language encoder. This renders our approach fully generative and makes it directly applicable to different "video+x to text" problems without the need to design specialized network heads for each task. The proposed framework is not only conceptually simple but also remarkably effective: experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks---captioning, question answering and audio-visual scene-aware dialog. Our code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Vx2Text_End-to-End_Learning_of_Video-Based_Text_Generation_From_Multimodal_Inputs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.12059 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Vx2Text_End-to-End_Learning_of_Video-Based_Text_Generation_From_Multimodal_Inputs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Vx2Text_End-to-End_Learning_of_Video-Based_Text_Generation_From_Multimodal_Inputs_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Vx2Text_End-to-End_Learning_CVPR_2021_supplemental.pdf | null |
KSM: Fast Multiple Task Adaption via Kernel-Wise Soft Mask Learning | Li Yang, Zhezhi He, Junshan Zhang, Deliang Fan | Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, and this is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback ) is proposed to address these issues by learning only a binary element-wise mask, while keeping the backbone model fixed. However, the binary mask has limited modeling capacity for new tasks. A more recent work proposes a compress-grow-based method (CPG) to achieve better accuracy for new tasks by partially training backbone model, but with order-higher training cost, which makes it infeasible to be deployed into popular state-of-the-art edge-/mobile-learning. The primary goal of this work is to simultaneously achieve fast and high-accuracy multi-task adaption in a continual learning setting. Thus motivated, we propose a new training method called Kernel-wise Soft Mask (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task. Such a soft mask can be viewed as a superposition of a binary mask and a properly scaled real-value tensor, which offers a richer representation capability without low-level kernel support to meet the objective of low hardware overhead. We validate KSM on multiple benchmark datasets against recent state-of-the-art methods (e.g. Piggyback, Packnet, CPG, etc.), which shows good improvement in both accuracy and training cost. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_KSM_Fast_Multiple_Task_Adaption_via_Kernel-Wise_Soft_Mask_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.05668 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_KSM_Fast_Multiple_Task_Adaption_via_Kernel-Wise_Soft_Mask_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_KSM_Fast_Multiple_Task_Adaption_via_Kernel-Wise_Soft_Mask_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Rich Context Aggregation With Reflection Prior for Glass Surface Detection | Jiaying Lin, Zebang He, Rynson W.H. Lau | Glass surfaces appear everywhere. Their existence can however pose a serious problem to computer vision tasks. Recently, a method is proposed to detect glass surfaces by learning multi-scale contextual information. However, as it is only based on a general context integration operation and does not consider any specific glass surface properties, it gets confused when the images contain objects that are similar to glass surfaces and degenerates in challenging scenes with insufficient contexts. We observe that humans often rely on identifying reflections in order to sense the existence of glass and on locating the boundary in order to determine the extent of the glass. Hence, we propose a model for glass surface detection, which consists of two novel modules: (1) a rich context aggregation module (RCAM) to extract multi-scale boundary features from rich context features for locating glass surface boundaries of different sizes and shapes, and (2) a reflection-based refinement module (RRM) to detect reflection and then incorporate it so as to differentiate glass regions from non-glass regions. In addition, we also propose a challenging dataset consisting of 4,012 glass images with annotations for glass surface detection. Our experiments demonstrate that the proposed model outperforms state-of-the-art methods from relevant fields. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Rich_Context_Aggregation_With_Reflection_Prior_for_Glass_Surface_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Rich_Context_Aggregation_With_Reflection_Prior_for_Glass_Surface_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Rich_Context_Aggregation_With_Reflection_Prior_for_Glass_Surface_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization | Aysim Toker, Qunjie Zhou, Maxim Maximov, Laura Leal-Taixe | The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. This task is notoriously challenging due to the drastic viewpoint and appearance differences between the two domains. We show that we can address this discrepancy explicitly by learning to synthesize realistic street views from satellite inputs. Following this observation, we propose a novel multi-task architecture in which image synthesis and retrieval are considered jointly. The rationale behind this is that we can bias our network to learn latent feature representations that are useful for retrieval if we utilize them to generate images across the two input domains. To the best of our knowledge, ours is the first approach that creates realistic street views from satellite images and localizes the corresponding query street view simultaneously in an end-to-end manner. In our experiments, we obtain state-of-the-art performance on the CVUSA and CVACT benchmarks. Finally, we show compelling qualitative results for satellite-to-street view synthesis. | https://openaccess.thecvf.com/content/CVPR2021/papers/Toker_Coming_Down_to_Earth_Satellite-to-Street_View_Synthesis_for_Geo-Localization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Toker_Coming_Down_to_Earth_Satellite-to-Street_View_Synthesis_for_Geo-Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Toker_Coming_Down_to_Earth_Satellite-to-Street_View_Synthesis_for_Geo-Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Toker_Coming_Down_to_CVPR_2021_supplemental.pdf | null |
AutoInt: Automatic Integration for Fast Neural Volume Rendering | David B. Lindell, Julien N. P. Martel, Gordon Wetzstein | Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10x with a tradeoff of reduced image quality. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lindell_AutoInt_Automatic_Integration_for_Fast_Neural_Volume_Rendering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01714 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lindell_AutoInt_Automatic_Integration_for_Fast_Neural_Volume_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lindell_AutoInt_Automatic_Integration_for_Fast_Neural_Volume_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lindell_AutoInt_Automatic_Integration_CVPR_2021_supplemental.zip | null |
Pose-Guided Human Animation From a Single Image in the Wild | Jae Shin Yoon, Lingjie Liu, Vladislav Golyanik, Kripasindhu Sarkar, Hyun Soo Park, Christian Theobalt | We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in preserving the identity and textures of the person. To address these limitations, we design a compositional neural network that predicts the silhouette, garment labels, and textures. Each modular network is explicitly dedicated to a subtask that can be learned from the synthetic data. At the inference time, we utilize the trained network to produce a unified representation of appearance and its labels in UV coordinates, which remain constant across poses. The unified representation provides incomplete yet strong guidance to generating the appearance in response to the pose change. We use the trained network to complete the appearance and render it with the background. With these strategies, we are able to synthesize human animations that can preserve the identity and appearance of the person in a temporally coherent way without any fine-tuning of the network on the testing scene. Experiments show that our method outperforms the state-of-the-arts in terms of synthesis quality, temporal coherence, and generalization ability. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03796 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yoon_Pose-Guided_Human_Animation_CVPR_2021_supplemental.pdf | null |
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression | Chen Gao, Jinyu Chen, Si Liu, Luting Wang, Qiong Zhang, Qi Wu | The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction. Different from related VLN tasks, the key to REVERIE is to conduct goal-oriented exploration instead of strict instruction-following, due to the lack of step-by-step navigation guidance. In this paper, we propose a novel Cross-modality Knowledge Reasoning (CKR) model to address the unique challenges of this task. The CKR, based on a transformer-architecture, learns to generate scene memory tokens and utilise these informative history clues for exploration. Particularly, a Room-and-Object Aware Attention (ROAA) mechanism is devised to explicitly perceive the room- and object-type information from both linguistic and visual observations. Moreover, through incorporating commonsense knowledge, we propose a Knowledge-enabled Entity Relationship Reasoning (KERR) module to learn the internal-external correlations among room- and object-entities for agent to make proper action at each viewpoint. Evaluation on REVERIE benchmark demonstrates the superiority of the CKR model, which significantly boosts SPL and REVERIE-success rate by 64.67% and 46.05%, respectively. Code is available at: https://github.com/alloldman/CKR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Room-and-Object_Aware_Knowledge_Reasoning_for_Remote_Embodied_Referring_Expression_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Room-and-Object_Aware_Knowledge_Reasoning_for_Remote_Embodied_Referring_Expression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Room-and-Object_Aware_Knowledge_Reasoning_for_Remote_Embodied_Referring_Expression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Room-and-Object_Aware_Knowledge_CVPR_2021_supplemental.pdf | null |
Equivariant Point Network for 3D Point Cloud Analysis | Haiwei Chen, Shichen Liu, Weikai Chen, Hao Li, Randall Hill | Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies. However, higher-order equivariant features often come with an exponentially-growing computational cost. Furthermore, it remains relatively less explored how rotation-equivariant features can be leveraged to tackle 3D shape alignment tasks. While many past approaches have been based on either non-equivariant or invariant descriptors to align 3D shapes, we argue that such tasks may benefit greatly from an equivariant framework. In this paper, we propose an effective and practical SE(3) (3D translation and rotation) equivariant network for point cloud analysis that addresses both problems. First, we present SE(3) separable point convolution, a novel framework that breaks down the 6D convolution into two separable convolutional operators alternatively performed in the 3D Euclidean and SO(3) spaces. This significantly reduces the computational cost without compromising the performance. Second, we introduce an attention layer to effectively harness the expressiveness of the equivariant features. While jointly trained with the network, the attention layer implicitly derives the intrinsic local frame in the feature space and generates attention vectors that can be integrated into different alignment tasks. We evaluate our approach through extensive studies and visual interpretations. The empirical results demonstrate that our proposed model outperforms strong baselines in a variety of benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14147 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Equivariant_Point_Network_CVPR_2021_supplemental.pdf | null |
Learning Graph Embeddings for Compositional Zero-Shot Learning | Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata | In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog)in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently from a car. As a solution, we propose a novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers, and latent representations of visual primitives in an end-to-end manner. The key to our approach is exploit-ing the dependency between states, objects, and their compositions within a graph structure to enforce the relevant knowledge transfer from seen to unseen compositions. By learning a joint compatibility that encodes semantics between concepts, our model allows for generalization to un-seen compositions without relying on an external knowledgebase like WordNet. We show that in the challenging generalized compositional zero-shot setting our CGE significantly outperforms the state of the art on MIT-States and UT-Zappos. We also propose a new benchmark for this task based on the recent GQA dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.01987 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Naeem_Learning_Graph_Embeddings_CVPR_2021_supplemental.pdf | null |
NeRD: Neural 3D Reflection Symmetry Detector | Yichao Zhou, Shichen Liu, Yi Ma | Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume the symmetry is given or detect the symmetry with a heuristic-based method. In this paper, we present NeRD, a Neural 3D Reflection Symmetry Detector, which combines the strength of learning-based recognition and geometry-based reconstruction to accurately recover the normal direction of objects' mirror planes. Specifically, we enumerate the symmetry planes with a coarse-to-fine strategy and find the best ones by building 3D cost volumes to examine the intra-image pixel correspondence from the symmetry. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression on both synthetic and real datasets. More importantly, we also demonstrate that the detected symmetry can be used to improve the performance of downstream tasks such as pose estimation and depth map regression by a wide margin over existing methods. The code of this paper has been made public at https://github.com/zhou13/nerd. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_NeRD_Neural_3D_Reflection_Symmetry_Detector_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.03211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_NeRD_Neural_3D_Reflection_Symmetry_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_NeRD_Neural_3D_Reflection_Symmetry_Detector_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_NeRD_Neural_3D_CVPR_2021_supplemental.pdf | null |
Checkerboard Context Model for Efficient Learned Image Compression | Dailan He, Yaoyan Zheng, Baocheng Sun, Yan Wang, Hongwei Qin | For learned image compression, the autoregressive context model is proved effective in improving the rate-distortion (RD) performance. Because it helps remove spatial redundancies among latent representations. However, the decoding process must be done in a strict scan order, which breaks the parallelization. We propose a parallelizable checkerboard context model (CCM) to solve the problem. Our two-pass checkerboard context calculation eliminates such limitations on spatial locations by re-organizing the decoding order. Speeding up the decoding process more than 40 times in our experiments, it achieves significantly improved computational efficiency with almost the same rate-distortion performance. To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Checkerboard_Context_Model_for_Efficient_Learned_Image_Compression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15306 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_Checkerboard_Context_Model_for_Efficient_Learned_Image_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_Checkerboard_Context_Model_for_Efficient_Learned_Image_Compression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Checkerboard_Context_Model_CVPR_2021_supplemental.pdf | null |
Zero-Shot Adversarial Quantization | Yuang Liu, Wei Zhang, Jun Wang | Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the high performance of full-precision models, most existing quantization methods focus on fine-tuning quantized model by assuming training datasets are accessible. However, this assumption sometimes is not satisfied in real situations due to data privacy and security issues, thereby making these quantization methods not applicable. To achieve zero-short model quantization without accessing training data, a tiny number of quantization methods adopt either post-training quantization or batch normalization statistics-guided data generation for fine-tuning. However, both of them inevitably suffer from low performance, since the former is a little too empirical and lacks training support for ultra-low precision quantization, while the latter could not fully restore the peculiarities of original data and is often low efficient for diverse data generation. To address the above issues, we propose a zero-shot adversarial quantization (ZAQ) framework, facilitating effective discrepancy estimation and knowledge transfer from a full-precision model to its quantized model. This is achieved by a novel two-level discrepancy modeling to drive a generator to synthesize informative and diverse data examples to optimize the quantized model in an adversarial learning fashion. We conduct extensive experiments on three fundamental vision tasks, demonstrating the superiority of ZAQ over the strong zero-shot baselines and validating the effectiveness of its main components. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15263 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.html | CVPR 2021 | null | null |
Group Whitening: Balancing Learning Efficiency and Representational Capacity | Lei Huang, Yi Zhou, Li Liu, Fan Zhu, Ling Shao | Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a model's learning efficiency can be further amplified by applying whitening, while its drawbacks in estimating population statistics for inference can be avoided through group normalization (GN). This paper proposes group whitening (GW), which exploits the advantages of the whitening operation and avoids the disadvantages of normalization within mini-batches. In addition, we analyze the constraints imposed on features by normalization, and show how the batch size (group number) affects the performance of batch (group) normalized networks, from the perspective of model's representational capacity. This analysis provides theoretical guidance for applying GW in practice. Finally, we apply the proposed GW to ResNet and ResNeXt architectures and conduct experiments on the ImageNet and COCO benchmarks. Results show that GW consistently improves the performance of different architectures, with absolute gains of 1.02% 1.49% in top-1 accuracy on ImageNet and 1.82% 3.21% in bounding box AP on COCO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Group_Whitening_Balancing_Learning_Efficiency_and_Representational_Capacity_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.13333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Group_Whitening_Balancing_Learning_Efficiency_and_Representational_Capacity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Group_Whitening_Balancing_Learning_Efficiency_and_Representational_Capacity_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Group_Whitening_Balancing_CVPR_2021_supplemental.pdf | null |
Adversarial Robustness Under Long-Tailed Distribution | Tong Wu, Ziwei Liu, Qingqiu Huang, Yu Wang, Dahua Lin | Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversarial robustness mainly focus on balanced datasets, while real-world data usually exhibits a long-tailed distribution. To push adversarial robustness towards more realistic scenarios, in this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. In particular, we first reveal the negative impacts induced by imbalanced data on both recognition performance and adversarial robustness, uncovering the intrinsic challenges of this problem. We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework. Several valuable observations are obtained: 1) natural accuracy is relatively easy to improve, 2) fake gain of robust accuracy exists under unreliable evaluation, and 3) boundary error limits the promotion of robustness. Inspired by these observations, we propose a clean yet effective framework, RoBal, which consists of two dedicated modules, a scale-invariant classifier and data re-balancing via both margin engineering at training stage and boundary adjustment during inference. Extensive experiments demonstrate the superiority of our approach over other state-of-the-art defense methods. To our best knowledge, we are the first to tackle adversarial robustness under long-tailed distributions, which we believe would be a significant step towards real-world robustness. Our code is available at: https://github.com/wutong16/Adversarial_Long-Tail. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02703 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Adversarial_Robustness_Under_CVPR_2021_supplemental.pdf | null |
HyperSeg: Patch-Wise Hypernetwork for Real-Time Semantic Segmentation | Yuval Nirkin, Lior Wolf, Tal Hassner | We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U-Net for drawing higher level context features, a multi-headed weight generating module which generates the weights of each block in the decoder immediately before they are consumed, for efficient memory utilization, and a primary network that is composed of novel dynamic patch-wise convolutions. Despite the usage of less-conventional blocks, our architecture obtains real-time performance. In terms of the runtime vs. accuracy trade-off, we surpass state of the art (SotA) results on popular semantic segmentation benchmarks: PASCAL VOC 2012 (val. set) and real-time semantic segmentation on Cityscapes, and CamVid. The code is available: https://nirkin.com/hyperseg. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_CVPR_2021_supplemental.pdf | null |
Augmentation Strategies for Learning With Noisy Labels | Kento Nishi, Yi Ding, Alex Rich, Tobias Hollerer | Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the ""learning with noisy labels"" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset Clothing1M. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the Clothing1M dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02130 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nishi_Augmentation_Strategies_for_CVPR_2021_supplemental.pdf | null |
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching | Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi | Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite poor. Addressing such problem, we present a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods for adaptive stereo matching, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design an efficient parameter-free cost normalization layer for internal feature-level alignment. Lastly, a highly related auxiliary task, self-supervised occlusion-aware reconstruction is presented to narrow down the gaps in output space. Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo, even outperforming disparity networks finetuned with target-domain ground-truths. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_AdaStereo_A_Simple_and_Efficient_Approach_for_Adaptive_Stereo_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.04627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdaStereo_A_Simple_and_Efficient_Approach_for_Adaptive_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdaStereo_A_Simple_and_Efficient_Approach_for_Adaptive_Stereo_Matching_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_AdaStereo_A_Simple_CVPR_2021_supplemental.pdf | null |
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic | Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong | We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capacities. Intuitively, smooth areas are easier to super-solve than complex textures. To utilize this property, we can adopt appropriate SR networks to process different sub-images after the decomposition. On this basis, we propose a new solution pipeline -- ClassSR that combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions. We further introduce a new classification method with two losses -- Class-Loss and Average-Loss to produce the classification results. After joint training, a majority of sub-images will pass through smaller networks, thus the computational cost can be significantly reduced. Experiments show that our ClassSR can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets. This general framework can also be applied in other low-level vision tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kong_ClassSR_A_General_Framework_to_Accelerate_Super-Resolution_Networks_by_Data_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04039 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kong_ClassSR_A_General_Framework_to_Accelerate_Super-Resolution_Networks_by_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kong_ClassSR_A_General_Framework_to_Accelerate_Super-Resolution_Networks_by_Data_CVPR_2021_paper.html | CVPR 2021 | null | null |
Partition-Guided GANs | Mohammadreza Armandpour, Ali Sadeghian, Chunyuan Li, Mingyuan Zhou | Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconnected set of manifolds. In this paper, we break down the challenging task of learning complex high dimensional distributions, supporting diverse data samples, to simpler sub-tasks. Our solution relies on designing a partitioner that breaks the space into smaller regions, each having a simpler distribution, and training a different generator for each partition. This is done in an unsupervised manner without requiring any labels. We formulate two desired criteria for the space partitioner that aid the training of our mixture of generators: 1) to produce connected partitions and 2) provide a proxy of distance between partitions and data samples, along with a direction for reducing that distance. These criteria are developed to avoid producing samples from places with non-existent data density, and also facilitate training by providing additional direction to the generators. We develop theoretical constraints for a space partitioner to satisfy the above criteria. Guided by our theoretical analysis, we design an effective neural architecture for the space partitioner that empirically assures these conditions. Experimental results on various standard benchmarks show that the proposed unsupervised model outperforms several recent methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Armandpour_Partition-Guided_GANs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00816 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Armandpour_Partition-Guided_GANs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Armandpour_Partition-Guided_GANs_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Armandpour_Partition-Guided_GANs_CVPR_2021_supplemental.pdf | null |
GATSBI: Generative Agent-Centric Spatio-Temporal Object Interaction | Cheol-Hui Min, Jinseok Bae, Junho Lee, Young Min Kim | We present GATSBI, a generative model that can transform a sequence of raw observations into a structured latent representation that fully captures the spatio-temporal context of the agent's actions. In vision-based decision-making scenarios, an agent faces complex high-dimensional observations where multiple entities interact with each other. The agent requires a good scene representation of the visual observation that discerns essential components that consistently propagates along the time horizon. Our method, GATSBI, utilizes unsupervised scene representation learning to successfully separate an active agent, static background, and passive objects. GATSBI then models the interactions reflecting the causal relationships among decomposed entities and predicts physically plausible future states. Our model generalizes to a variety of environments where different types of robots and objects dynamically interact with each other. GATSBI achieves superior performance on scene decompo-sition and video prediction compared to its state-of-the-artcounterparts, and can be readily applied to sequential deci-sion making of an intelligent agent. | https://openaccess.thecvf.com/content/CVPR2021/papers/Min_GATSBI_Generative_Agent-Centric_Spatio-Temporal_Object_Interaction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04275 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Min_GATSBI_Generative_Agent-Centric_Spatio-Temporal_Object_Interaction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Min_GATSBI_Generative_Agent-Centric_Spatio-Temporal_Object_Interaction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Min_GATSBI_Generative_Agent-Centric_CVPR_2021_supplemental.zip | null |
Privacy-Preserving Collaborative Learning With Automatic Transformation Search | Wei Gao, Shangwei Guo, Tianwei Zhang, Han Qiu, Yonggang Wen, Yang Liu | Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired. In this paper, we propose to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients. We design a novel search method to automatically discover qualified policies. We adopt two new metrics to quantify the impacts of transformations on data privacy and model usability, which can significantly accelerate the search speed. Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Privacy-Preserving_Collaborative_Learning_With_Automatic_Transformation_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12505 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Privacy-Preserving_Collaborative_Learning_With_Automatic_Transformation_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Privacy-Preserving_Collaborative_Learning_With_Automatic_Transformation_Search_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval | Yawen Zeng, Da Cao, Xiaochi Wei, Meng Liu, Zhou Zhao, Zheng Qin | Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and query sentence is intuitive and efficient for understanding semantics but is rarely considered. Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.html | CVPR 2021 | null | null |
Point Cloud Instance Segmentation Using Probabilistic Embeddings | Biao Zhang, Peter Wonka | In this paper, we propose a new framework for point cloud instance segmentation. Our framework has two steps: an embedding step and a clustering step. In the embedding step, our main contribution is to propose a probabilistic embedding space for point cloud embedding. Specifically, each point is represented as a tri-variate normal distribution. In the clustering step, we propose a novel loss function, which benefits both the semantic segmentation and the clustering. Our experimental results show important improvements to the SOTA, i.e., 3.1% increased average per-category mAP on the PartNet dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.pdf | http://arxiv.org/abs/1912.00145 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Point_Cloud_Instance_CVPR_2021_supplemental.zip | null |
pixelNeRF: Neural Radiance Fields From One or Few Images | Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa | We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields (NeRFs) involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. We take a step towards resolving these shortcomings by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This allows the network to be trained across multiple scenes to learn a scene prior, allowing it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one). Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks under category specific and category agnostic settings. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes as well as real scenes from the DTU dataset. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_pixelNeRF_Neural_Radiance_Fields_From_One_or_Few_Images_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02190 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_pixelNeRF_Neural_Radiance_Fields_From_One_or_Few_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_pixelNeRF_Neural_Radiance_Fields_From_One_or_Few_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_pixelNeRF_Neural_Radiance_CVPR_2021_supplemental.pdf | null |
Navigating the GAN Parameter Space for Semantic Image Editing | Anton Cherepkov, Andrey Voynov, Artem Babenko | Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines. Furthermore, GANs are especially useful for controllable generation since their latent spaces contain a wide range of interpretable directions, well suited for semantic editing operations. By gradually changing latent codes along these directions, one can produce impressive visual effects, unattainable without GANs. In this paper, we significantly expand the range of visual effects achievable with the state-of-the-art models, like StyleGAN2. In contrast to existing works, which mostly operate by latent codes, we discover interpretable directions in the space of the generator parameters. By several simple methods, we explore this space and demonstrate that it also contains a plethora of interpretable directions, which are an excellent source of non-trivial semantic manipulations. The discovered manipulations cannot be achieved by transforming the latent codes and can be used to edit both synthetic and real images. We release our code and models and hope they will serve as a handy tool for further efforts on GAN-based image editing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cherepkov_Navigating_the_GAN_Parameter_Space_for_Semantic_Image_Editing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13786 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cherepkov_Navigating_the_GAN_Parameter_Space_for_Semantic_Image_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cherepkov_Navigating_the_GAN_Parameter_Space_for_Semantic_Image_Editing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cherepkov_Navigating_the_GAN_CVPR_2021_supplemental.pdf | null |
Large-Capacity Image Steganography Based on Invertible Neural Networks | Shao-Ping Lu, Rong Wang, Tao Zhong, Paul L. Rosin | Many attempts have been made to hide information in images, where the main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Large-Capacity_Image_Steganography_CVPR_2021_supplemental.pdf | null |
Exploiting Edge-Oriented Reasoning for 3D Point-Based Scene Graph Analysis | Chaoyi Zhang, Jianhui Yu, Yang Song, Weidong Cai | Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGGpoint) framework to effectively bridge perception and reasoning to achieve scene understanding via three sequential stages, namely scene graph construction, reasoning, and inference. Within the reasoning stage, an EDGE-oriented Graph Convolutional Network (EdgeGCN) is created to exploit multi-dimensional edge features for explicit relationship modeling, together with the exploration of two associated twinning interaction mechanisms between nodes and edges for the independent evolution of scene graph representations. Overall, our integrated SGGpoint framework is established to seek and infer scene structures of interest from both real-world and synthetic 3D point-based scenes. Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies. We also demonstrate our method advantage on several traditional graph representation learning benchmark datasets, including the node-wise classification on citation networks and whole-graph recognition problems for molecular analysis. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Exploiting_Edge-Oriented_Reasoning_for_3D_Point-Based_Scene_Graph_Analysis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Exploiting_Edge-Oriented_Reasoning_for_3D_Point-Based_Scene_Graph_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Exploiting_Edge-Oriented_Reasoning_for_3D_Point-Based_Scene_Graph_Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Exploiting_Edge-Oriented_Reasoning_CVPR_2021_supplemental.pdf | null |
CoLA: Weakly-Supervised Temporal Action Localization With Snippet Contrastive Learning | Can Zhang, Meng Cao, Dongming Yang, Jie Chen, Yuexian Zou | Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the "localization by classification" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet (or frame) individually and thus overlook the fruitful temporal context relation. Here arises the single snippet cheating issue: "hard" snippets are too vague to be classified. In this paper, we argue that learning by comparing helps identify these hard snippets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short. Specifically, we propose a Snippet Contrast (SniCo) Loss to refine the hard snippet representation in feature space, which guides the network to perceive precise temporal boundaries and avoid the temporal interval interruption. Besides, since it is infeasible to access frame-level annotations, we introduce a Hard Snippet Mining algorithm to locate the potential hard snippets. Substantial analyses verify that this mining strategy efficaciously captures the hard snippets and SniCo Loss leads to more informative feature representation. Extensive experiments show that CoLA achieves state-of-the-art results on THUMOS'14 and ActivityNet v1.2 datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16392 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_CoLA_Weakly-Supervised_Temporal_CVPR_2021_supplemental.zip | null |
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition | Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng | Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes. This imbalance degrades the performance of typical supervised learning algorithms designed for balanced training sets. In this paper, we address this issue by augmenting minority classes with a recently proposed implicit semantic data augmentation (ISDA) algorithm, which produces diversified augmented samples by translating deep features along many semantically meaningful directions. Importantly, given that ISDA estimates the class-conditional statistics to obtain semantic directions, we find it ineffective to do this on minority classes due to the insufficient training data. To this end, we propose a novel approach to learn transformed semantic directions with meta-learning automatically. In specific, the augmentation strategy during training is dynamically optimized, aiming to minimize the loss on a small balanced validation set, which is approximated via a meta update step. Extensive empirical results on CIFAR-LT-10/100, ImageNet-LT, and iNaturalist 2017/2018 validate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_MetaSAug_Meta_Semantic_Augmentation_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12579 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_MetaSAug_Meta_Semantic_Augmentation_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_MetaSAug_Meta_Semantic_Augmentation_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
Limitations of Post-Hoc Feature Alignment for Robustness | Collin Burns, Jacob Steinhardt | Feature alignment is an approach to improving robustness to distribution shift that matches the distribution of feature activations between the training distribution and test distribution. A particularly simple but effective approach to feature alignment involves aligning the batch normalization statistics between the two distributions in a trained neural network. This technique has received renewed interest lately because of its impressive performance on robustness benchmarks. However, when and why this method works is not well understood. We investigate the approach in more detail and identify several limitations. We show that it only significantly helps with a narrow set of distribution shifts and we identify several settings in which it even degrades performance. We also explain why these limitations arise by pinpointing why this approach can be so effective in the first place. Our findings call into question the utility of this approach and Unsupervised Domain Adaptation more broadly for improving robustness in practice. | https://openaccess.thecvf.com/content/CVPR2021/papers/Burns_Limitations_of_Post-Hoc_Feature_Alignment_for_Robustness_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05898 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Burns_Limitations_of_Post-Hoc_Feature_Alignment_for_Robustness_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Burns_Limitations_of_Post-Hoc_Feature_Alignment_for_Robustness_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Burns_Limitations_of_Post-Hoc_CVPR_2021_supplemental.pdf | null |
Every Annotation Counts: Multi-Label Deep Supervision for Medical Image Segmentation | Simon Reiss, Constantin Seibold, Alexander Freytag, Erik Rodner, Rainer Stiefelhagen | Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field. Providing representative and accurate annotations is often mission-critical especially for challenging medical applications. In this paper, we propose a semi-weakly supervised segmentation algorithm to overcome this barrier. Our approach is based on a new formulation of deep supervision and student-teacher model and allows for easy integration of different supervision signals. In contrast to previous work, we show that care has to be taken how deep supervision is integrated in lower layers and we present multi-label deep supervision as the most important secret ingredient for success. With our novel training regime for segmentation that flexibly makes use of images that are either fully labeled, marked with bounding boxes, just global labels, or not at all, we are able to cut the requirement for expensive labels by 94.22% - narrowing the gap to the best fully supervised baseline to only 5% mean IoU. Our approach is validated by extensive experiments on retinal fluid segmentation and we provide an in-depth analysis of the anticipated effect each annotation type can have in boosting segmentation performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Reiss_Every_Annotation_Counts_Multi-Label_Deep_Supervision_for_Medical_Image_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.13243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_Every_Annotation_Counts_Multi-Label_Deep_Supervision_for_Medical_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_Every_Annotation_Counts_Multi-Label_Deep_Supervision_for_Medical_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reiss_Every_Annotation_Counts_CVPR_2021_supplemental.pdf | null |
Roses Are Red, Violets Are Blue... but Should VQA Expect Them To? | Corentin Kervadec, Grigory Antipov, Moez Baccouche, Christian Wolf | Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to ""reason"", leading them to perform ""educated guesses"" instead. In this paper, we claim that the standard evaluation metric, which consists in measuring the overall in-domain accuracy, is misleading. Since questions and concepts are unbalanced, this tends to favor models which exploit subtle training set statistics. Alternatively, naively introducing artificial distribution shifts between train and test splits is also not completely satisfying. First, the shifts do not reflect real-world tendencies, resulting in unsuitable models; second, since the shifts are handcrafted, trained models are specifically designed for this particular setting, and do not generalize to other configurations. We propose the GQA-OOD benchmark designed to overcome these concerns: we measure and compare accuracy over both rare and frequent question-answer pairs, and argue that the former is better suited to the evaluation of reasoning abilities, which we experimentally validate with models trained to more or less exploit biases. In a large-scale study involving 7 VQA models and 3 bias reduction techniques, we also experimentally demonstrate that these models fail to address questions involving infrequent concepts and provide recommendations for future directions of research. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kervadec_Roses_Are_Red_Violets_Are_Blue..._but_Should_VQA_Expect_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.05121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kervadec_Roses_Are_Red_Violets_Are_Blue..._but_Should_VQA_Expect_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kervadec_Roses_Are_Red_Violets_Are_Blue..._but_Should_VQA_Expect_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kervadec_Roses_Are_Red_CVPR_2021_supplemental.pdf | null |
FAPIS: A Few-Shot Anchor-Free Part-Based Instance Segmenter | Khoi Nguyen, Sinisa Todorovic | This paper is about few-shot instance segmentation, where training and test image sets do not share the same object classes. We specify and evaluate a new few-shot anchor-free part-based instance segmenter (FAPIS). Our key novelty is in explicit modeling of latent object parts shared across training object classes, which is expected to facilitate our few-shot learning on new classes in testing. We specify a new anchor-free object detector aimed at scoring and regressing locations of foreground bounding boxes, as well as estimating relative importance of latent parts within each box. Also, we specify a new network for delineating and weighting latent parts for the final instance segmentation within every detected bounding box. Our evaluation on the benchmark COCO-20i dataset demonstrates that we significantly outperform the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_FAPIS_A_Few-Shot_Anchor-Free_Part-Based_Instance_Segmenter_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00073 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_FAPIS_A_Few-Shot_Anchor-Free_Part-Based_Instance_Segmenter_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_FAPIS_A_Few-Shot_Anchor-Free_Part-Based_Instance_Segmenter_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nguyen_FAPIS_A_Few-Shot_CVPR_2021_supplemental.pdf | null |
Disentangling Label Distribution for Long-Tailed Visual Recognition | Youngkyu Hong, Seungju Han, Kwanghee Choi, Seokjun Seo, Beomsu Kim, Buru Chang | The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution. Such protocol has questionable practicality since the target may also be long-tailed. Therefore, we formulate long-tailed visual recognition as a label shift problem where the target and source label distributions are different. One of the significant hurdles in dealing with the label shift problem is the entanglement between the source label distribution and the model prediction. In this paper, we focus on disentangling the source label distribution from the model prediction. We first introduce a simple but overlooked baseline method that matches the target label distribution by post-processing the model prediction trained by the cross-entropy loss and the Softmax function. Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase. Thus, we propose a novel method, LAbel distribution DisEntangling (LADE) loss based on the optimal bound of Donsker-Varadhan representation. LADE achieves state-of-the-art performance on benchmark datasets such as CIFAR-100-LT, Places-LT, ImageNet-LT, and iNaturalist 2018. Moreover, LADE outperforms existing methods on various shifted target label distributions, showing the general adaptability of our proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Disentangling_Label_Distribution_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00321 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Disentangling_Label_Distribution_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Disentangling_Label_Distribution_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Disentangling_Label_Distribution_CVPR_2021_supplemental.pdf | null |
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling | Mateusz Malinowski, Dimitrios Vytiniotis, Grzegorz Swirszcz, Viorica Patraucean, Joao Carreira | How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time learning. It also creates a coupling between consecutive layers, which limits model parallelism and increases memory consumption. In this paper, we build upon Sideways, which avoids blocking by propagating approximate gradients forward in time, by proposing mechanisms for temporal integration of information based on different variants of skip connections. We also show how to decouple computation and delegate individual neural modules to different devices, allowing distributed and parallel training. The proposed Skip-sideways achieves low latency training, model parallelism, and, importantly, is capable of extracting temporal features, leading to more stable training and improved performance on real-world video datasets such as HMDB51, UCF101, and the large-scale Kinetics600. Finally, we also show that models trained with Skip-sideways generate better future frames than Sideways models, and hence they can better utilize motion cues. | https://openaccess.thecvf.com/content/CVPR2021/papers/Malinowski_Gradient_Forward-Propagation_for_Large-Scale_Temporal_Video_Modelling_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.08318 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Malinowski_Gradient_Forward-Propagation_for_Large-Scale_Temporal_Video_Modelling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Malinowski_Gradient_Forward-Propagation_for_Large-Scale_Temporal_Video_Modelling_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Malinowski_Gradient_Forward-Propagation_for_CVPR_2021_supplemental.pdf | null |
Learning a Non-Blind Deblurring Network for Night Blurry Images | Liang Chen, Jiawei Zhang, Jinshan Pan, Songnan Lin, Faming Fang, Jimmy S. Ren | Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels to the deconvolution steps that are further optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters for better image restoration . The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_a_Non-Blind_Deblurring_Network_for_Night_Blurry_Images_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_a_Non-Blind_Deblurring_Network_for_Night_Blurry_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_a_Non-Blind_Deblurring_Network_for_Night_Blurry_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Learning_a_Non-Blind_CVPR_2021_supplemental.pdf | null |
Differentiable Diffusion for Dense Depth Estimation From Multi-View Images | Numair Khan, Min H. Kim, James Tompkin | We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view reprojection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further, we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Khan_Differentiable_Diffusion_for_Dense_Depth_Estimation_From_Multi-View_Images_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.08917 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Khan_Differentiable_Diffusion_for_Dense_Depth_Estimation_From_Multi-View_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Khan_Differentiable_Diffusion_for_Dense_Depth_Estimation_From_Multi-View_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Khan_Differentiable_Diffusion_for_CVPR_2021_supplemental.pdf | null |
Deep Compositional Metric Learning | Wenzhao Zheng, Chengkun Wang, Jiwen Lu, Jie Zhou | In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminative loss to enlarge interclass distances while suppressing intraclass variations, which might lead to inferior generalization performance since samples even from the same class may present diverse characteristics. This motivates the adoption of the ensemble technique to learn a number of sub-embeddings using different and diverse subtasks. However, most subtasks impose weaker or contradictory constraints, which essentially sacrifices the discrimination ability of each sub-embedding to improve the generalization ability of their combination. To achieve a better generalization ability without compromising, we propose to separate the sub-embeddings from direct supervisions from the subtasks and apply the losses on different composites of the sub-embeddings. We employ a set of learnable compositors to combine the sub-embeddings and use a self-reinforced loss to train the compositors, which serve as relays to distribute the diverse training signals to avoid destroying the discrimination ability. Experimental results on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the superior performance of our framework. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Deep_Compositional_Metric_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Compositional_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Compositional_Metric_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Representing Videos As Discriminative Sub-Graphs for Action Recognition | Dong Li, Zhaofan Qiu, Yingwei Pan, Ting Yao, Houqiang Li, Tao Mei | Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training | Mihai Fieraru, Mihai Zanfir, Silviu Cristian Pirlea, Vlad Olaru, Cristian Sminchisescu | I went to the gym today, but how well did I do? And where should I improve? Ah, my back hurts slightly... User engagement can be sustained and injuries avoided by being able to reconstruct 3d human pose and motion, relate it to good training practices, identify errors, and provide early, real-time feedback. In this paper we introduce the first automatic system, AIFit, that performs 3d human sensing for fitness training. The system can be used at home, outdoors, or at the gym. AIFit is able to reconstruct 3d human pose and motion, reliably segment exercise repetitions, and identify in real-time the deviations between standards learnt from trainers, and the execution of a trainee. As a result, localized, quantitative feedback for correct execution of exercises, reduced risk of injury, and continuous improvement is possible. To support research and evaluation, we introduce the first large scale dataset, Fit3D, containing over 3 million images and corresponding 3d human shape and motion capture ground truth configurations, with over 37 repeated exercises, covering all the major muscle groups, performed by instructors and trainees. Our statistical coach is governed by a global parameter that captures how critical it should be of a trainee's performance. This is an important aspect that helps adapt to a student's level of fitness (i.e. beginner vs. advanced vs. expert), or to the expected accuracy of a 3d pose reconstruction method. We show that, for different values of the global parameter, our feedback system based on 3d pose estimates achieves good accuracy compared to the one based on ground-truth motion capture. Our statistical coach offers feedback in natural language, and with spatio-temporal visual grounding. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fieraru_AIFit_Automatic_3D_Human-Interpretable_Feedback_Models_for_Fitness_Training_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fieraru_AIFit_Automatic_3D_Human-Interpretable_Feedback_Models_for_Fitness_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fieraru_AIFit_Automatic_3D_Human-Interpretable_Feedback_Models_for_Fitness_Training_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fieraru_AIFit_Automatic_3D_CVPR_2021_supplemental.zip | null |
Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes | Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu, Xiaolong Wang | Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordances. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework which synthesizes long-term 3D human motion conditioning on the 3D scene structure. We also further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Synthesizing_Long-Term_3D_Human_Motion_and_Interaction_in_3D_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05522 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Synthesizing_Long-Term_3D_Human_Motion_and_Interaction_in_3D_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Synthesizing_Long-Term_3D_Human_Motion_and_Interaction_in_3D_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Synthesizing_Long-Term_3D_CVPR_2021_supplemental.pdf | null |
How Well Do Self-Supervised Models Transfer? | Linus Ericsson, Henry Gouk, Timothy M. Hospedales | Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction. We compare their performance to a supervised baseline and show that on most tasks the best self-supervised models outperform supervision, confirming the recently observed trend in the literature. We find ImageNet Top-1 accuracy to be highly correlated with transfer to many-shot recognition, but increasingly less so for few-shot, object detection and dense prediction. No single self-supervised method dominates overall, suggesting that universal pre-training is still unsolved. Our analysis of features suggests that top self-supervised learners fail to preserve colour information as well as supervised alternatives, but tend to induce better classifier calibration, and less attentive overfitting than supervised learners. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ericsson_How_Well_Do_CVPR_2021_supplemental.pdf | null |
Understanding Object Dynamics for Interactive Image-to-Video Synthesis | Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Bjorn Ommer | What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available at https://bit.ly/3cxfA2L. | https://openaccess.thecvf.com/content/CVPR2021/papers/Blattmann_Understanding_Object_Dynamics_for_Interactive_Image-to-Video_Synthesis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.11303 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Blattmann_Understanding_Object_Dynamics_for_Interactive_Image-to-Video_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Blattmann_Understanding_Object_Dynamics_for_Interactive_Image-to-Video_Synthesis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Blattmann_Understanding_Object_Dynamics_CVPR_2021_supplemental.zip | null |
Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis | Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, Gordon Wetzstein | We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks (p-GAN or pi-GAN), for high-quality 3D-aware image synthesis. p-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent radiance fields. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chan_Pi-GAN_Periodic_Implicit_CVPR_2021_supplemental.pdf | null |
Diverse Branch Block: Building a Convolution as an Inception-Like Unit | Xiaohan Ding, Xiangyu Zhang, Jungong Han, Guiguang Ding | We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https://github.com/DingXiaoH/DiverseBranchBlock. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_Diverse_Branch_Block_Building_a_Convolution_as_an_Inception-Like_Unit_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13425 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Diverse_Branch_Block_Building_a_Convolution_as_an_Inception-Like_Unit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Diverse_Branch_Block_Building_a_Convolution_as_an_Inception-Like_Unit_CVPR_2021_paper.html | CVPR 2021 | null | null |
Post-Hoc Uncertainty Calibration for Domain Drift Scenarios | Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem, Daniel Cremers, Florian Buettner | We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date, the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly over-confident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tomani_Post-Hoc_Uncertainty_Calibration_for_Domain_Drift_Scenarios_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.10988 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tomani_Post-Hoc_Uncertainty_Calibration_for_Domain_Drift_Scenarios_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tomani_Post-Hoc_Uncertainty_Calibration_for_Domain_Drift_Scenarios_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tomani_Post-Hoc_Uncertainty_Calibration_CVPR_2021_supplemental.pdf | null |
Slimmable Compressive Autoencoders for Practical Neural Image Compression | Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov | Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders (SlimCAEs), where rate (R) and distortion (D) are jointly optimized for different capacities. Once trained, encoders and decoders can be executed at different capacities, leading to different rates and complexities. We show that a successful implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs. Our experiments show that SlimCAEs are highly flexible models that provide excellent rate-distortion performance, variable rate, and dynamic adjustment of memory, computational cost and latency, thus addressing the main requirements of practical image compression. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Slimmable_Compressive_Autoencoders_for_Practical_Neural_Image_Compression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15726 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Slimmable_Compressive_Autoencoders_for_Practical_Neural_Image_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Slimmable_Compressive_Autoencoders_for_Practical_Neural_Image_Compression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Slimmable_Compressive_Autoencoders_CVPR_2021_supplemental.pdf | null |
Function4D: Real-Time Human Volumetric Capture From Very Sparse Consumer RGBD Sensors | Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, Yebin Liu | Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a human volumetric capture method that combines temporal volumetric fusion and deep implicit functions. To achieve high-quality and temporal-continuous reconstruction, we propose dynamic sliding fusion to fuse neighboring depth observations together with topology consistency. Moreover, for detailed and complete surface generation, we propose detail-preserving deep implicit functions for RGBD input which can not only preserve the geometric details on the depth inputs but also generate more plausible texturing results. Results and experiments show that our method outperforms existing methods in terms of view sparsity, generalization capacity, reconstruction quality, and run-time efficiency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Function4D_Real-Time_Human_Volumetric_Capture_From_Very_Sparse_Consumer_RGBD_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.01859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Function4D_Real-Time_Human_Volumetric_Capture_From_Very_Sparse_Consumer_RGBD_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Function4D_Real-Time_Human_Volumetric_Capture_From_Very_Sparse_Consumer_RGBD_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Function4D_Real-Time_Human_CVPR_2021_supplemental.pdf | null |
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution | Xin Deng, Hao Wang, Mai Xu, Yichen Guo, Yuhang Song, Li Yang | The omnidirectional images (ODIs) are usually at low-resolution, due to the constraints of collection, storage and transmission. The traditional two-dimensional (2D) image super-resolution methods are not effective for spherical ODIs, because ODIs tend to have non-uniformly distributed pixel density and varying texture complexity across latitudes. In this work, we propose a novel latitude adaptive upscaling network (LAU-Net) for ODI super-resolution, which allows pixels at different latitudes to adopt distinct upscaling factors. Specifically, we introduce a Laplacian multi-level separation architecture to split an ODI into different latitude bands, and hierarchically upscale them with different factors. In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands. To the best of our knowledge, LAU-Net is the first attempt to consider the latitude difference for ODI super-resolution. Extensive results demonstrate that our LAU-Net significantly advances the super-resolution performance for ODIs. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_LAU-Net_Latitude_Adaptive_Upscaling_Network_for_Omnidirectional_Image_Super-Resolution_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LAU-Net_Latitude_Adaptive_Upscaling_Network_for_Omnidirectional_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LAU-Net_Latitude_Adaptive_Upscaling_Network_for_Omnidirectional_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | null | null |
UP-DETR: Unsupervised Pre-Training for Object Detection With Transformers | Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen | Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre-training transformers in natural language processing, we propose a pretext task named random query patch detection to Unsupervisedly Pre-train DETR (UP-DETR) for object detection. Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation. Code and pre-training models: https://github.com/dddzg/up-detr. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_UP-DETR_Unsupervised_Pre-Training_for_Object_Detection_With_Transformers_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_UP-DETR_Unsupervised_Pre-Training_for_Object_Detection_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_UP-DETR_Unsupervised_Pre-Training_for_Object_Detection_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_UP-DETR_Unsupervised_Pre-Training_CVPR_2021_supplemental.pdf | null |
Self-Attention Based Text Knowledge Mining for Text Detection | Qi Wan, Haoqin Ji, Linlin Shen | Pre-trained models play an important role in deep learning based text detectors. However, most methods ignore the gap between natural images and scene text images and directly apply ImageNet for pre-training. To address such a problem, some of them firstly pre-train the model using a large amount of synthetic data and then fine-tune it on target datasets, which is task-specific and has limited generalization capability. In this paper, we focus on providing general pre-trained models for text detectors. Considering the importance of exploring text contents for text detection, we propose STKM (Self-attention based Text Knowledge Mining), which consists of a CNN Encoder and a Self-attention Decoder, to learn general prior knowledge for text detection from SynthText. Given only image level text labels, Self-attention Decoder directly decodes features extracted from CNN Encoder to texts without requirement of detection, which guides the CNN backbone to explicitly learn discriminative semantic representations ignored by previous approaches. After that, the text knowledge learned by the backbone can be transferred to various text detectors to significantly improve their detection performance (e.g., 5.89% higher F-measure for EAST on ICDAR15 dataset) without bells and whistles. Pre-trained model is available at: https://github.com/CVI-SZU/STKM | https://openaccess.thecvf.com/content/CVPR2021/papers/Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wan_Self-Attention_Based_Text_CVPR_2021_supplemental.pdf | null |
Image De-Raining via Continual Learning | Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, Zheng-Jun Zha | While deep convolutional neural networks (CNNs) have achieved great success on image de-raining task, most existing methods can only learn fixed mapping rules between paired rainy/clean images on a single dataset. This limits their applications in practical situations with multiple and incremental datasets where the mapping rules may change for different types of rain streaks. However, the catastrophic forgetting of traditional deep CNN model challenges the design of generalized framework for multiple and incremental datasets. A strategy of sharing the network structure but independently updating and storing the network parameters on each dataset has been developed as a potential solution. Nevertheless, this strategy is not applicable to compact systems as it dramatically increases the overall training time and parameter space. To alleviate such limitation, in this study, we propose a parameter importance guided weights modification approach, named PIGWM. Specifically, with new dataset (e.g. new rain dataset), the well-trained network weights are updated according to their importance evaluated on previous training dataset. With extensive experimental validation, we demonstrate that a single network with a single parameter set of our proposed method can process multiple rain datasets almost without performance degradation. The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks. The results indicate that our proposed method has great potential for other computer vision tasks with dynamic learning environments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Layer-Wise Searching for 1-Bit Detectors | Sheng Xu, Junhe Zhao, Jinhu Lu, Baochang Zhang, Shumin Han, David Doermann | 1-bit detectors show great promise for resource-constrained embedded devices but often suffer from a significant performance gap compared with their real-valued counterparts. The primary reason lies in the layer-wise error during binarization. This paper presents a layer-wise search (LWS) strategy to generate 1-bit detectors that maintain a performance very close to the original real-valued model. The approach introduces angular and amplitude angular error loss functions to increase detector capacity. At each layer, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework. It then fine-tunes the scale parameter of that layer to reduce the amplitude error. Extensive experiments show that LWS-Det outperforms state-of-the-art 1-bit detectors by a considerable margin on the PASCAL VOC and COCO datasets. For example, the LWS-Det achieves 1-bit Faster-RCNN with ResNet-34 backbone within 2.0% mAP of its real-valued counterpart on the PASCAL VOC dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Layer-Wise_Searching_for_1-Bit_Detectors_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layer-Wise_Searching_for_1-Bit_Detectors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layer-Wise_Searching_for_1-Bit_Detectors_CVPR_2021_paper.html | CVPR 2021 | null | null |
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning | Yanbei Chen, Yongqin Xian, A. Sophia Koepke, Ying Shan, Zeynep Akata | Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directly aligning the representations of different modalities, we compose audio, image, and video representations across modalities to uncover the richer multi-modal knowledge. Our main idea is to learn a compositional embedding that closes the cross-modal semantic gap and captures the task-relevant semantics, which facilitates pulling together representations across modalities by compositional contrastive learning. We establish a new, comprehensive multi-modal distillation benchmark on three video datasets: UCF101, ActivityNet, and VGGSound. Moreover, we demonstrate that our model significantly outperforms a variety of existing knowledge distillation methods in transferring audio-visual knowledge to improve video representation learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Distilling_Audio-Visual_Knowledge_by_Compositional_Contrastive_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10955 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Audio-Visual_Knowledge_by_Compositional_Contrastive_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Audio-Visual_Knowledge_by_Compositional_Contrastive_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Distilling_Audio-Visual_Knowledge_CVPR_2021_supplemental.pdf | null |
Unsupervised Visual Attention and Invariance for Reinforcement Learning | Xudong Wang, Long Lian, Stella X. Yu | The vision-based reinforcement learning (RL) has achieved tremendous success. However, generalizing vision-based RL policy to unknown test environments still remains as a challenging problem. Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing ""clean"" observations for the RL policy. The proposed unsupervised visual attention and invariance method (VAI) contains three key components: 1) an unsupervised keypoint detection model which captures semantically meaningful keypoints in observations; 2) an unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation; 3) a self-supervised adapter for visual distraction invariance which reconstructs distraction-invariant attention mask from observations with artificial disturbances generated by a series of foreground and background augmentations. All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free observations to RL policy. VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15% 49% in DeepMind Control suite benchmark and 61% 229% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Visual_Attention_and_Invariance_for_Reinforcement_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02921 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Visual_Attention_and_Invariance_for_Reinforcement_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Visual_Attention_and_Invariance_for_Reinforcement_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Visual_Attention_CVPR_2021_supplemental.pdf | null |
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