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DualGraph: A Graph-Based Method for Reasoning About Label Noise | HaiYang Zhang, XiMing Xing, Liang Liu | Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based methods. However, for numerous studies on account of the above two views, selected samples cannot take full advantage of all data points and cannot represent actual distribution of categories, in particular if label annotation is corrupted. In this paper, we start from a different perspective and propose a robust learning algorithm called DualGraph, which aims to capture structural relations among labels at two different levels with graph neural networks including instance-level and distribution-level relations. Specifically, the instance-level relation utilizes instance similarity characterize sample category, while the distribution-level relation describes instance similarity distribution from each sample to all other samples. Since the distribution-level relation is robust to label noise, our network propagates it as supervised signals to refine instance-level similarity. Combining two level relations, we design an end-to-end training paradigm to counteract noisy labels while generating reliable predictions. We conduct extensive experiments on the noisy CIFAR-10 dataset, CIFAR-100 dataset, and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method in comparison to state-of-the-art baselines. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DualGraph_A_Graph-Based_Method_for_Reasoning_About_Label_Noise_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DualGraph_A_Graph-Based_Method_for_Reasoning_About_Label_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DualGraph_A_Graph-Based_Method_for_Reasoning_About_Label_Noise_CVPR_2021_paper.html | CVPR 2021 | null | null |
Automatic Correction of Internal Units in Generative Neural Networks | Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi | Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a number of generated images with defective visual patterns which are known as artifacts. While most of the recent work tries to fix artifact generations by perturbing latent code, few investigate internal units of a generator to fix them. In this work, we devise a method that automatically identifies the internal units generating various types of artifact images. We further propose the sequential correction algorithm which adjusts the generation flow by modifying the detected artifact units to improve the quality of generation while preserving the original outline. Our method outperforms the baseline method in terms of FID-score and shows satisfactory results with human evaluation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tousi_Automatic_Correction_of_Internal_Units_in_Generative_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06118 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tousi_Automatic_Correction_of_Internal_Units_in_Generative_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tousi_Automatic_Correction_of_Internal_Units_in_Generative_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tousi_Automatic_Correction_of_CVPR_2021_supplemental.pdf | null |
Generating Manga From Illustrations via Mimicking Manga Creation Workflow | Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu | We present a framework to generate manga from digital illustrations. In professional mange studios, the manga create workflow consists of three key steps: (1) Artists use line drawings to delineate the structural outlines in manga storyboards. (2) Artists apply several types of regular screentones to render the shading, occlusion, and object materials. (3) Artists selectively paste irregular screen textures onto the canvas to achieve various background layouts or special effects. Motivated by this workflow, we propose a data-driven framework to convert a digital illustration into three corresponding components: manga line drawing, regular screentone, and irregular screen texture. These components can be directly composed into manga images and can be further retouched for more plentiful manga creations. To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner. We conduct both perceptual user study and qualitative evaluation of the generated manga, and observe that our generated image layers for these three components are practically usable in the daily works of manga artists. We provide 60 qualitative results and 15 additional comparisons in the supplementary material. We will make our presented manga dataset publicly available to assist related applications. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Generating_Manga_From_Illustrations_via_Mimicking_Manga_Creation_Workflow_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Generating_Manga_From_Illustrations_via_Mimicking_Manga_Creation_Workflow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Generating_Manga_From_Illustrations_via_Mimicking_Manga_Creation_Workflow_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Decoding Deraining Network and Quasi-Sparsity Based Training | Yinglong Wang, Chao Ma, Bing Zeng | Existing deep deraining models are mainly learned via directly minimizing the statistical differences between rainy images and rain-free ground truths. They emphasize learning a mapping from rainy images to rain-free images with supervision. Despite the demonstrated success, these methods do not perform well on restoring the fine-grained local details or removing blurry rainy traces. In this work, we aim to exploit the intrinsic priors of rainy images and develop intrinsic loss functions to facilitate training deraining networks, which decompose a rainy image into a rain-free background layer and a rainy layer containing intact rain streaks. To this end, we introduce the quasi-sparsity prior to train network so as to generate two sparse layers with intact textures of different objects. Then we explore the low-value prior to compensate sparsity, forcing all rain streaks to enter into one layer while non-rain contents into another layer to restore image details. We introduce a multi-decoding structure to specially supervise the generation of multi-type deraining features. This helps to learn the most contributory features to deraining in respective spaces. Moreover, our model stabilizes the feature values from multi-spaces via information sharing to alleviate potential artifacts, which also accelerates the running speed. Extensive experiments show that the proposed deraining method outperforms the state-of-the-art approaches in terms of effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Multi-Decoding_Deraining_Network_and_Quasi-Sparsity_Based_Training_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Multi-Decoding_Deraining_Network_and_Quasi-Sparsity_Based_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Multi-Decoding_Deraining_Network_and_Quasi-Sparsity_Based_Training_CVPR_2021_paper.html | CVPR 2021 | null | null |
Open-Vocabulary Object Detection Using Captions | Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, Shih-Fu Chang | Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10678 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zareian_Open-Vocabulary_Object_Detection_CVPR_2021_supplemental.pdf | null |
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization | Xingjia Pan, Yingguo Gao, Zhiwen Lin, Fan Tang, Weiming Dong, Haolei Yuan, Feiyue Huang, Changsheng Xu | Weakly supervised object localization (WSOL) remains an open problem due to the deficiency of finding object extent information using a classification network. While prior works struggle to localize objects by various spatial regularization strategies, we argue that how to extract object structural information from the trained classification network is neglected. In this paper, we propose a two-stage approach, termed structure-preserving activation (SPA), towards fully leveraging the structure information incorporated in convolutional features for WSOL. In the first stage, a restricted activation module (RAM) is designed to alleviate the structure-missing issue caused by the classification network, based on the observation that the unbounded classification map and global average pooling layer drive the network to focus only on object parts. In the second stage, we propose a post-process approach, termed the self-correlation map generating (SCG) module to obtain structure-preserving localization maps on the basis of the activation maps acquired from the first stage. Specifically, we utilize the high-order self-correlation (HSC) to extract the inherent structural information retained in the learned model and then aggregate HSC of multiple points for precise object localization. Extensive experiments on two publicly available benchmarks including CUB-200-2011 and ILSVRC show that the proposed SPA achieves substantial and consistent performance gains compared with baseline approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_Unveiling_the_Potential_of_Structure_Preserving_for_Weakly_Supervised_Object_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Unveiling_the_Potential_of_Structure_Preserving_for_Weakly_Supervised_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Unveiling_the_Potential_of_Structure_Preserving_for_Weakly_Supervised_Object_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pan_Unveiling_the_Potential_CVPR_2021_supplemental.pdf | null |
From Points to Multi-Object 3D Reconstruction | Francis Engelmann, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari | We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. The key idea is to optimize for detection, alignment and shape jointly over all objects in the RGB image, while focusing on realistic and physically plausible reconstructions. To this end, we propose a key-point detector that localizes objects as center points and directly predicts all object properties, including 9-DoF bounding boxes and 3D shapes, all in a single forward pass. The method formulates 3D shape reconstruction as a shape selection problem, i.e. it selects among exemplar shapes from a given database. This makes it agnostic to shape representations, which enables a lightweight reconstruction of realistic and visually-pleasing shapes based on CAD-models, while the training objective is formulated around point clouds and voxel representations. A collision-loss promotes non-intersecting objects, further increasing the reconstruction realism. Given the RGB image, the presented approach performs lightweight reconstruction in a single-stage, it is real-time capable, fully differentiable and end-to-end trainable. Our experiments compare multiple approaches for 9-DoF bounding box estimation, evaluate the novel shape-selection mechanism and compare to recent methods in terms of 3D bounding box estimation and 3D shape reconstruction quality. | https://openaccess.thecvf.com/content/CVPR2021/papers/Engelmann_From_Points_to_Multi-Object_3D_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11575 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Engelmann_From_Points_to_Multi-Object_3D_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Engelmann_From_Points_to_Multi-Object_3D_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dual-Stream Multiple Instance Learning Network for Whole Slide Image Classification With Self-Supervised Contrastive Learning | Bin Li, Yin Li, Kevin W. Eliceiri | We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dual-Stream_Multiple_Instance_Learning_Network_for_Whole_Slide_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08939 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dual-Stream_Multiple_Instance_Learning_Network_for_Whole_Slide_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dual-Stream_Multiple_Instance_Learning_Network_for_Whole_Slide_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Dual-Stream_Multiple_Instance_CVPR_2021_supplemental.pdf | null |
Regressive Domain Adaptation for Unsupervised Keypoint Detection | Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long | Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of them are tailored into classification settings and may fail in regression tasks, especially in the practical keypoint detection task. To tackle this difficult but significant task, we present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Inspired by the latest theoretical work, we first utilize an adversarial regressor to maximize the disparity on the target domain and train a feature generator to minimize this disparity. However, due to the high dimension of the output space, this regressor fails to detect samples that deviate from the support of the source. To overcome this problem, we propose two important ideas. First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor. Second, to alleviate the optimization difficulty in the high-dimensional space, we innovatively convert the minimax game in the adversarial training to the minimization of two opposite goals. Extensive experiments show that our method brings large improvement by 8% to 11% in terms of PCK on different datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Regressive_Domain_Adaptation_for_Unsupervised_Keypoint_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Regressive_Domain_Adaptation_for_Unsupervised_Keypoint_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Regressive_Domain_Adaptation_for_Unsupervised_Keypoint_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mask Guided Matting via Progressive Refinement Network | Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, Alan Yuille | We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced in the training to further enhance its robustness to external guidance. We show that PRN can generalize to unseen types of guidance masks such as trimap and low-quality alpha matte, making it suitable for various application pipelines. In addition, we revisit the foreground color prediction problem for matting and propose a surprisingly simple improvement to address the dataset issue. Evaluation on real and synthetic benchmarks shows that MG Matting achieves state-of-the-art performance using various types of guidance inputs. Code and models are available at https://github.com/yucornetto/MGMatting. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Mask_Guided_Matting_via_Progressive_Refinement_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06722 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Mask_Guided_Matting_via_Progressive_Refinement_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Mask_Guided_Matting_via_Progressive_Refinement_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
Monocular Reconstruction of Neural Face Reflectance Fields | Mallikarjun B R, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, Christian Theobalt | The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component. This still leaves out important perceptual aspects of reflectance such as higher-order global illumination effects and self-shadowing. We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a monocular image. Instead of modeling each component of the reflectance separately using parametric models, our neural representation allows us to generate a basis set of faces in a geometric deformation-invariant space, parameterized by the input light direction, viewpoint and face geometry. We learn to reconstruct this reflectance field of a face just from a monocular image, which can be used to render the face from any viewpoint in any light condition. Our method is trained on a light-stage dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods due to better capturing of physical effects, such as sub-surface scattering, specularities, self-shadows and other higher-order effects. | https://openaccess.thecvf.com/content/CVPR2021/papers/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.10247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/R_Monocular_Reconstruction_of_CVPR_2021_supplemental.pdf | null |
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network | Xu Yang, Cheng Deng, Zhiyuan Dang, Kun Wei, Junchi Yan | Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_SelfSAGCN_Self-Supervised_Semantic_Alignment_for_Graph_Convolution_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_SelfSAGCN_Self-Supervised_Semantic_Alignment_for_Graph_Convolution_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_SelfSAGCN_Self-Supervised_Semantic_Alignment_for_Graph_Convolution_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-Shot Learning | Chaofan Chen, Xiaoshan Yang, Changsheng Xu, Xuhui Huang, Zhe Ma | Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_ECKPN_Explicit_Class_Knowledge_Propagation_Network_for_Transductive_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.08523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_ECKPN_Explicit_Class_Knowledge_Propagation_Network_for_Transductive_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_ECKPN_Explicit_Class_Knowledge_Propagation_Network_for_Transductive_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Coarse-Fine Networks for Temporal Activity Detection in Videos | Kumara Kahatapitiya, Michael S. Ryoo | In this paper, we introduce 'Coarse-Fine Networks', a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations for long-term motion. Traditional Video models process inputs at one (or few) fixed temporal resolution without any dynamic frame selection. However, we argue that, processing multiple temporal resolutions of the input and doing so dynamically by learning to estimate the importance of each frame can largely improve video representations, specially in the domain of temporal activity localization. To this end, we propose (1) 'Grid Pool', a learned temporal downsampling layer to extract coarse features, and, (2) 'Multi-stage Fusion', a spatio-temporal attention mechanism to fuse a fine-grained context with the coarse features. We show that our method outperforms the state-of-the-arts for action detection in public datasets including Charades with a significantly reduced compute and memory footprint. The code is available at https://github.com/kkahatapitiya/Coarse-Fine-Networks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kahatapitiya_Coarse-Fine_Networks_for_Temporal_Activity_Detection_in_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kahatapitiya_Coarse-Fine_Networks_for_Temporal_Activity_Detection_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kahatapitiya_Coarse-Fine_Networks_for_Temporal_Activity_Detection_in_Videos_CVPR_2021_paper.html | CVPR 2021 | null | null |
Can Audio-Visual Integration Strengthen Robustness Under Multimodal Attacks? | Yapeng Tian, Chenliang Xu | In this paper, we propose to make a systematic study on machines' multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks; audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks; even a weakly-supervised sound source visual localization model can be successfully fooled; our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Can_Audio-Visual_Integration_Strengthen_Robustness_Under_Multimodal_Attacks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02000 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Can_Audio-Visual_Integration_Strengthen_Robustness_Under_Multimodal_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Can_Audio-Visual_Integration_Strengthen_Robustness_Under_Multimodal_Attacks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Can_Audio-Visual_Integration_CVPR_2021_supplemental.pdf | null |
Deep Gradient Projection Networks for Pan-sharpening | Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, Chunxia Zhang | Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively. The codes are available at https://github.com/xsxjtu/GPPNN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04584 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.html | CVPR 2021 | null | null |
ReNAS: Relativistic Evaluation of Neural Architecture Search | Yixing Xu, Yunhe Wang, Kai Han, Yehui Tang, Shangling Jui, Chunjing Xu, Chang Xu | An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing NAS algorithms often train and evaluate intermediate neural architectures on a small proxy dataset with limited training epochs. But it is difficult to expect an accurate performance estimation of an architecture in such a coarse evaluation way. This paper advocates a new neural architecture evaluation scheme, which aims to determine which architecture would perform better instead of accurately predict the absolute architecture performance. Therefore, we propose a relativistic architecture performance predictor in NAS (ReNAS). We encode neural architectures into feature tensors, and further refining the representations with the predictor. The proposed relativistic performance predictor can be deployed in discrete searching methods to search for the desired architectures without additional evaluation. Experimental results on NAS-Bench-101 dataset suggests that, sampling 424 (0.1% of the entire search space) neural architectures and their corresponding validation performance is already enough for learning an accurate architecture performance predictor. The accuracies of our searched neural architectures on NAS-Bench-101 and NAS-Bench-201 datasets are higher than that of the state-of-the-art methods and show the priority of the proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_ReNAS_Relativistic_Evaluation_of_Neural_Architecture_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/1910.01523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ReNAS_Relativistic_Evaluation_of_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ReNAS_Relativistic_Evaluation_of_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_ReNAS_Relativistic_Evaluation_CVPR_2021_supplemental.pdf | null |
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks | Jiahang Wang, Sheng Jin, Wentao Liu, Weizhong Liu, Chen Qian, Ping Luo | Human pose estimation is a fundamental yet challenging task in computer vision, which aims at localizing human anatomical keypoints. However, unlike human vision that is robust to various data corruptions such as blur and pixelation, current pose estimators are easily confused by these corruptions. This work comprehensively studies and addresses this problem by building rigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate the weaknesses of current advanced pose estimators, and a new algorithm termed AdvMix is proposed to improve their robustness in different corruptions. Our work has several unique benefits. (1) AdvMix is model-agnostic and capable in a wide-spectrum of pose estimation models. (2) AdvMix consists of adversarial augmentation and knowledge distillation. Adversarial augmentation contains two neural network modules that are trained jointly and competitively in an adversarial manner, where a generator network mixes different corrupted images to confuse a pose estimator, improving the robustness of the pose estimator by learning from harder samples. To compensate for the noise patterns by adversarial augmentation, knowledge distillation is applied to transfer clean pose structure knowledge to the target pose estimator. (3) Extensive experiments show that AdvMix significantly increases the robustness of pose estimations across a wide range of corruptions, while maintaining accuracy on clean data in various challenging benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_When_Human_Pose_Estimation_Meets_Robustness_Adversarial_Algorithms_and_Benchmarks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.06152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_When_Human_Pose_Estimation_Meets_Robustness_Adversarial_Algorithms_and_Benchmarks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_When_Human_Pose_Estimation_Meets_Robustness_Adversarial_Algorithms_and_Benchmarks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_When_Human_Pose_CVPR_2021_supplemental.pdf | null |
ReMix: Towards Image-to-Image Translation With Limited Data | Jie Cao, Luanxuan Hou, Ming-Hsuan Yang, Ran He, Zhenan Sun | Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples. The generator learns to translate the in-between samples rather than memorizing the training set, and thereby forces the discriminator to generalize. The proposed approach effectively reduces the ambiguity of generation and renders content-preserving results. The ReMix method can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ReMix method achieve significant improvements. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_ReMix_Towards_Image-to-Image_Translation_With_Limited_Data_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_ReMix_Towards_Image-to-Image_Translation_With_Limited_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_ReMix_Towards_Image-to-Image_Translation_With_Limited_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cao_ReMix_Towards_Image-to-Image_CVPR_2021_supplemental.pdf | null |
Adaptive Rank Estimate in Robust Principal Component Analysis | Zhengqin Xu, Rui He, Shoulie Xie, Shiqian Wu | Robust principal component analysis (RPCA) and its variants have gained wide applications in computer vision. However, these methods either involve manual adjustment of some parameters, or require the rank of a low-rank matrix to be known a prior. In this paper, an adaptive rank estimate based RPCA (ARE-RPCA) is proposed, which adaptively assigns weights on different singular values via rank estimation. More specifically, we study the characteristics of the low-rank matrix, and develop an improved Gerschgorin disk theorem to estimate the rank of the low-rank matrix accurately. Furthermore in view of the issue occurred in the Gerschgorin disk theorem that adjustment factor need to be manually pre-defined, an adaptive setting method, which greatly facilitates the practical implementation of the rank estimation, is presented. Then, the weights of singular values in the nuclear norm are updated adaptively based on iteratively estimated rank, and the resultant low-rank matrix is close to the target. Experimental results show that the proposed ARE-RPCA outperforms the state-of-the-art methods in various complex scenarios. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Adaptive_Rank_Estimate_in_Robust_Principal_Component_Analysis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Adaptive_Rank_Estimate_in_Robust_Principal_Component_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Adaptive_Rank_Estimate_in_Robust_Principal_Component_Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Adaptive_Rank_Estimate_CVPR_2021_supplemental.zip | null |
Continual Adaptation of Visual Representations via Domain Randomization and Meta-Learning | Riccardo Volpi, Diane Larlus, Gregory Rogez | Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature -- the well-known "catastrophic forgetting" issue. In particular, when a model consecutively learns from different visual domains, it tends to forget the past domains in favor of the most recent ones. In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization -- for vision tasks, randomizing the current domain's distribution with heavy image manipulations. Building on this result, we devise a meta-learning strategy where a regularizer explicitly penalizes any loss associated with transferring the model from the current domain to different "auxiliary" meta-domains, while also easing adaptation to them. Such meta-domains are also generated through randomized image manipulations. We empirically demonstrate in a variety of experiments -- spanning from classification to semantic segmentation -- that our approach results in models that are less prone to catastrophic forgetting when transferred to new domains. | https://openaccess.thecvf.com/content/CVPR2021/papers/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04324 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Volpi_Continual_Adaptation_of_Visual_Representations_via_Domain_Randomization_and_Meta-Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Volpi_Continual_Adaptation_of_CVPR_2021_supplemental.pdf | null |
DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance | Kaihua Zhang, Mingliang Dong, Bo Liu, Xiao-Tong Yuan, Qingshan Liu | The objective of co-saliency detection is to segment the co-occurring salient objects in a group of images. To address this task, we introduce a new deep network architecture via semantic-aware contrast Gromov-Wasserstein distance (DeepACG). We first adopt the Gromov-Wasserstein (GW) distance to build dense hierarchical 4D correlation volumes for all pairs of image pixels within the image group. This dense correlation volumes enables the network to accurately discover the structured pair-wise pixel similarities among the common salient objects. Second, we develop a semantic-aware co-attention module (SCAM) to enhance the foreground saliency through predicted categorical information. Specifically, SCAM recognizes the semantic class of the foreground objects; and this information is then projected to the deep representations to localize the related pixels. Third, we design a contrast edge enhanced module (EEM) to capture richer context and preserve fine-grained spatial information. We validate the effectiveness of our model using three popular benchmark datasets (Cosal2015, CoSOD3k and CoCA). Extensive experiments have demonstrated the substantial practical merit of each module. Compared with the existing works, DeepACG shows significant improvements and achieves state-of-the-art performance. Code will be made available soon. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DeepACG_Co-Saliency_Detection_via_Semantic-Aware_Contrast_Gromov-Wasserstein_Distance_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DeepACG_Co-Saliency_Detection_via_Semantic-Aware_Contrast_Gromov-Wasserstein_Distance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DeepACG_Co-Saliency_Detection_via_Semantic-Aware_Contrast_Gromov-Wasserstein_Distance_CVPR_2021_paper.html | CVPR 2021 | null | null |
SurFree: A Fast Surrogate-Free Black-Box Attack | Thibault Maho, Teddy Furon, Erwan Le Merrer | Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively close to their originals. Last couple of years have witnessed a striking decrease in the amount of queries a black box attack submits to the target classifier, in order to forge adversarials. This particularly concerns the black box score-based setup, where the attacker has access to top predicted probabilites: the amount of queries went from to millions of to less than a thousand. This paper presents SurFree, a geometrical approach that achieves a similar drastic reduction in the amount of queries in the hardest setup: black box decision-based attacks (only the top-1 label is available). We first highlight that the most recent attacks in that setup, HSJA, QEBA and GeoDA all perform costly gradient surrogate estimations. SurFree proposes to bypass these, by instead focusing on careful trials along diverse directions, guided by precise indications of geometrical properties of the classifier decision boundaries. We motivate this geometric approach before performing a head-to-head comparison with previous attacks with the amount of queries as a first class citizen. We exhibit a faster distortion decay under low query amounts (few hundreds to a thousand), while remaining competitive at higher query budgets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Maho_SurFree_A_Fast_Surrogate-Free_Black-Box_Attack_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12807 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Maho_SurFree_A_Fast_Surrogate-Free_Black-Box_Attack_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Maho_SurFree_A_Fast_Surrogate-Free_Black-Box_Attack_CVPR_2021_paper.html | CVPR 2021 | null | null |
Beyond Image to Depth: Improving Depth Prediction Using Echoes | Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma | We address the problem of estimating depth with multi modal audio visual data. Inspired by the ability of animals, such as bats and dolphins, to infer distance of objects with echolocation, some recent methods have utilized echoes for depth estimation. We propose an end-to-end deep learning based pipeline utilizing RGB images, binaural echoes and estimated material properties of various objects within a scene. We argue that the relation between image, echoes and depth, for different scene elements, is greatly influenced by the properties of those elements, and a method designed to leverage this information can lead to significantly improved depth estimation from audio visual inputs. We propose a novel multi modal fusion technique, which incorporates the material properties explicitly while combining audio (echoes) and visual modalities to predict the scene depth. We show empirically, with experiments on Replica dataset, that the proposed method obtains 28% improvement in RMSE compared to the state-of-the-art audio-visual depth prediction method. To demonstrate the effectiveness of our method on larger dataset, we report competitive performance on Matterport3D, proposing to use it as a multi modal depth prediction benchmark with echoes for the first time. We also analyse the proposed method with exhaustive ablation experiments and qualitative results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Parida_Beyond_Image_to_Depth_Improving_Depth_Prediction_Using_Echoes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Parida_Beyond_Image_to_Depth_Improving_Depth_Prediction_Using_Echoes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Parida_Beyond_Image_to_Depth_Improving_Depth_Prediction_Using_Echoes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Parida_Beyond_Image_to_CVPR_2021_supplemental.pdf | null |
Rich Features for Perceptual Quality Assessment of UGC Videos | Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang | Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In this paper, we create a large scale dataset to comprehensively investigate characteristics of generic UGC video quality. Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality. Our model is able to provide quality scores as well as human-friendly quality indicators, to bridge the gap between low level video signals to human perceptual quality. Experimental results show that our model achieves state-of-the-art correlation with Mean Opinion Scores (MOS). | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Rich_Features_for_CVPR_2021_supplemental.pdf | null |
Sequential Graph Convolutional Network for Active Learning | Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim | We propose a novel pool-based Active Learning frame-work constructed on a sequential Graph Convolution Net-work (GCN). Each image's feature from a pool of data rep-resents a node in the graph and the edges encode their similarities. With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes by minimising the binary cross-entropy loss. GCN performs message-passing operations between the nodes, and hence, induces similar representations of the strongly associated nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from la-belled ones. To this end, we utilise the graph node embed-dings and their confidence scores and adapt sampling techniques such as CoreSet and uncertainty-based methods to query the nodes. We flip the label of newly queried nodes from unlabelled to labelled, re-train the learner to optimise the downstream task and the graph to minimise its modified objective. We continue this process within a fixed budget. We evaluate our method on 6 different benchmarks: 4 real image classification, 1 depth-based hand pose estimation and 1 synthetic RGB image classification datasets. Our method outperforms several competitive baselines such as VAAL, Learning Loss, CoreSet and attains the new state-of-the-art performance on multiple applications. | https://openaccess.thecvf.com/content/CVPR2021/papers/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.10219 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Caramalau_Sequential_Graph_Convolutional_CVPR_2021_supplemental.pdf | null |
Generative Classifiers as a Basis for Trustworthy Image Classification | Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, Carsten Rother | With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainability and robustness. Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities. However, this has mostly been demonstrated on simple datasets such as MNIST and CIFAR in the past. In this work, we firstly develop an architecture and training scheme that allows GCs to operate on a more relevant level of complexity for practical computer vision, namely the ImageNet challenge. Secondly, we demonstrate the immense potential of GCs for trustworthy image classification. Explainability and some aspects of robustness are vastly improved compared to feed-forward models, even when the GCs are just applied naively. While not all trustworthiness problems are solved completely, we observe that GCs are a highly promising basis for further algorithms and modifications. We release our trained model for download in the hope that it serves as a starting point for other generative classification tasks, in much the same way as pretrained ResNet architectures do for discriminative classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mackowiak_Generative_Classifiers_as_a_Basis_for_Trustworthy_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.15036 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mackowiak_Generative_Classifiers_as_a_Basis_for_Trustworthy_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mackowiak_Generative_Classifiers_as_a_Basis_for_Trustworthy_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mackowiak_Generative_Classifiers_as_CVPR_2021_supplemental.pdf | null |
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation | Yang Jiao, Trac D. Tran, Guangming Shi | This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow F, stereo-depth D, camera pose P and motion segmentation S. Our key insight is that the rigidity of the scene shares the same inherent geometrical structure with object movements and scene depth. Hence, rigidity from S can be inferred by jointly coupling F, D and S to achieve more robust estimation. To this end, we propose a novel scene flow framework named EffiScene with efficient joint rigidity learning, going beyond the existing pipeline with independent auxiliary structures. In EffiScene, we first estimate optical flow and depth at the coarse level and then compute camera pose by Perspective-n-Points method. To jointly learn local rigidity, we design a novel Rigidity From Motion (RfM) layer with three principal components: (i) correlation extraction; (ii) boundary learning; and (iii) outlier exclusion. Final outputs are fused based on the rigid map M_R from RfM at finer levels. To efficiently train EffiScene, two new losses L_bnd and L_unc are designed to prevent trivial solutions and to regularize the flow boundary discontinuity. Extensive experiments on scene flow benchmark KITTI show that our method is effective and significantly improves the state-of-the-art approaches for all sub-tasks, i.e. optical flow (5.19 -> 4.20), depth estimation (3.78 -> 3.46), visual odometry (0.012 -> 0.011) and motion segmentation (0.57 -> 0.62). | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.html | CVPR 2021 | null | null |
Localizing Visual Sounds the Hard Way | Honglie Chen, Weidi Xie, Triantafyllos Afouras, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman | The objective of this work is to localize sound sources that are visible in a video without using manual annotations. Our key technical contribution is to show that, by training the network to explicitly discriminate challenging image fragments, even for images that do contain the object emitting the sound, we can significantly boost the localization performance. We do so elegantly by introducing a mechanism to mine hard samples and add them to a contrastive learning formulation automatically. We show that our algorithm achieves state-of-the-art performance on the popular Flickr SoundNet dataset. Furthermore, we introduce the VGG-Sound Source (VGG-SS) benchmark, a new set of annotations for the recently-introduced VGG-Sound dataset, where the sound sources visible in each video clip are explicitly marked with bounding box annotations. This dataset is 20 times larger than analogous existing ones, contains 5K videos spanning over 200 categories, and, differently from Flickr SoundNet, is video-based. On VGG-SS, we also show that our algorithm achieves state-of-the-art performance against several baselines. Code and datasets can be found at http://www.robots.ox.ac.uk/ vgg/research/lvs/ | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Localizing_Visual_Sounds_the_Hard_Way_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02691 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Localizing_Visual_Sounds_the_Hard_Way_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Localizing_Visual_Sounds_the_Hard_Way_CVPR_2021_paper.html | CVPR 2021 | null | null |
Synthesize-It-Classifier: Learning a Generative Classifier Through Recurrent Self-Analysis | Arghya Pal, Raphael C.-W. Phan, KokSheik Wong | In this work, we show the generative capability of an image classifier network by synthesizing high-resolution, photo-realistic, and diverse images at scale. The overall methodology, called Synthesize-It-Classifier (STIC), does not require an explicit generator network to estimate the density of the data distribution and sample images from that, but instead uses the classifier's knowledge of the boundary to perform gradient ascent w.r.t. class logits and then synthesizes images using Gram Matrix Metropolis Adjusted Langevin Algorithm (GRMALA) by drawing on a blank canvas. During training, the classifier iteratively uses these synthesized images as fake samples and re-estimates the class boundary in a recurrent fashion to improve both the classification accuracy and quality of synthetic images. The STIC shows that mixing of the hard fake samples (i.e. those synthesized by the one hot class conditioning), and the soft fake samples (which are synthesized as a convex combination of classes, i.e. a mixup of classes) improves class interpolation. We demonstrate an Attentive-STIC network that shows iterative drawing of synthesized images on the ImageNet dataset that has thousands of classes. In addition, we introduce the synthesis using a class conditional score classifier (Score-STIC) instead of a normal image classifier and show improved results on several real world datasets, i.e. ImageNet, LSUN and CIFAR 10. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pal_Synthesize-It-Classifier_Learning_a_Generative_Classifier_Through_Recurrent_Self-Analysis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pal_Synthesize-It-Classifier_Learning_a_Generative_Classifier_Through_Recurrent_Self-Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pal_Synthesize-It-Classifier_Learning_a_Generative_Classifier_Through_Recurrent_Self-Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pal_Synthesize-It-Classifier_Learning_a_CVPR_2021_supplemental.pdf | null |
Self-Point-Flow: Self-Supervised Scene Flow Estimation From Point Clouds With Optimal Transport and Random Walk | Ruibo Li, Guosheng Lin, Lihua Xie | Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences by applying point-wise matching that only takes the distance on 3D point coordinates into account, introducing two critical issues: (1) it overlooks other discriminative measures, such as color and surface normal, which often bring fruitful clues for accurate matching; and (2) it often generates sub-par performance, as the matching is operated in an unconstrained situation, where multiple points can be ended up with the same corresponding point. To address the issues, we formulate this matching task as an optimal transport problem. The output optimal assignment matrix can be utilized to guide the generation of pseudo ground truth. In this optimal transport, we design the transport cost by considering multiple descriptors and encourage one-to-one matching by mass equality constraints. Also, constructing a graph on the points, a random walk module is introduced to encourage the local consistency of the pseudo labels. Comprehensive experiments on FlyingThings3D and KITTI show that our method achieves state-of-the-art performance among self-supervised learning methods. Our self-supervised method even performs on par with some supervised learning approaches, although we do not need any ground truth flow for training. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Self-Point-Flow_Self-Supervised_Scene_CVPR_2021_supplemental.pdf | null |
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation | Yunhang Shen, Liujuan Cao, Zhiwei Chen, Feihong Lian, Baochang Zhang, Chi Su, Yongjian Wu, Feiyue Huang, Rongrong Ji | Panoptic segmentation aims to partition an image to object instances and semantic content for thing and stuff categories, respectively. To date, learning weakly supervised panoptic segmentation (WSPS) with only image-level labels remains unexplored. In this paper, we propose an efficient jointly thing-and-stuff mining (JTSM) framework for WSPS. To this end, we design a novel mask of interest pooling (MoIPool) to extract fixed-size pixel-accurate feature maps of arbitrary-shape segmentations. MoIPool enables a panoptic mining branch to leverage multiple instance learning (MIL) to recognize things and stuff segmentation in a unified manner. We further refine segmentation masks with parallel instance and semantic segmentation branches via self-training, which collaborates the mined masks from panoptic mining with bottom-up object evidence as pseudo-ground-truth labels to improve spatial coherence and contour localization. Experimental results demonstrate the effectiveness of JTSM on PASCAL VOC and MS COCO. As a by-product, we achieve competitive results for weakly supervised object detection and instance segmentation. This work is a first step towards tackling challenge panoptic segmentation task with only image-level labels. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Toward_Joint_Thing-and-Stuff_Mining_for_Weakly_Supervised_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Toward_Joint_Thing-and-Stuff_Mining_for_Weakly_Supervised_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Toward_Joint_Thing-and-Stuff_Mining_for_Weakly_Supervised_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals | Yiyue Luo, Yunzhu Li, Michael Foshey, Wan Shou, Pratyusha Sharma, Tomas Palacios, Antonio Torralba, Wojciech Matusik | Daily human activities, e.g., locomotion, exercises, and resting, are heavily guided by the tactile interactions between the human and the ground. In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input. We build a low-cost, high-density, large-scale intelligent carpet, which enables the real-time recordings of human-floor tactile interactions in a seamless manner. We collect a synchronized tactile and visual dataset on various human activities. Employing a state-of-the-art camera-based pose estimation model as supervision, we design and implement a deep neural network model to infer 3D human poses using only the tactile information. Our pipeline can be further scaled up to multi-person pose estimation. We evaluate our system and demonstrate its potential applications in diverse fields. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Intelligent_Carpet_Inferring_3D_Human_Pose_From_Tactile_Signals_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Intelligent_Carpet_Inferring_3D_Human_Pose_From_Tactile_Signals_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Intelligent_Carpet_Inferring_3D_Human_Pose_From_Tactile_Signals_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Intelligent_Carpet_Inferring_CVPR_2021_supplemental.zip | null |
Railroad Is Not a Train: Saliency As Pseudo-Pixel Supervision for Weakly Supervised Semantic Segmentation | Seungho Lee, Minhyun Lee, Jongwuk Lee, Hyunjung Shim | Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, namely Explicit Pseudo-pixel Supervision (EPS), which learns from pixel-level feedback by combining two weak supervisions; the image-level label provides the object identity via the localization map and the saliency map from the off-the-shelf saliency detection model offers rich boundaries. We devise a joint training strategy to fully utilize the complementary relationship between both information. Our method can obtain accurate object boundaries and discard co-occurring pixels, thereby significantly improving the quality of pseudo-masks. Experimental results show that the proposed method remarkably outperforms existing methods by resolving key challenges of WSSS and achieves the new state-of-the-art performance on both PASCAL VOC 2012 and MS COCO 2014 datasets. The code is available at https://github.com/halbielee/EPS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Railroad_Is_Not_a_Train_Saliency_As_Pseudo-Pixel_Supervision_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.08965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Railroad_Is_Not_a_Train_Saliency_As_Pseudo-Pixel_Supervision_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Railroad_Is_Not_a_Train_Saliency_As_Pseudo-Pixel_Supervision_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Railroad_Is_Not_CVPR_2021_supplemental.zip | null |
Stable View Synthesis | Gernot Riegler, Vladlen Koltun | We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operates on a geometric scaffold computed via structure-from-motion and multi-view stereo. Each point on this 3D scaffold is associated with view rays and corresponding feature vectors that encode the appearance of this point in the input images. The core of SVS is view-dependent on-surface feature aggregation, in which directional feature vectors at each 3D point are processed to produce a new feature vector for a ray that maps this point into the new target view. The target view is then rendered by a convolutional network from a tensor of features synthesized in this way for all pixels. The method is composed of differentiable modules and is trained end-to-end. It supports spatially-varying view-dependent importance weighting and feature transformation of source images at each point; spatial and temporal stability due to the smooth dependence of on-surface feature aggregation on the target view; and synthesis of view-dependent effects such as specular reflection. Experimental results demonstrate that SVS outperforms state-of-the-art view synthesis methods both quantitatively and qualitatively on three diverse real-world datasets, achieving unprecedented levels of realism in free-viewpoint video of challenging large-scale scenes. Code is available at https://github.com/intel-isl/StableViewSynthesis | https://openaccess.thecvf.com/content/CVPR2021/papers/Riegler_Stable_View_Synthesis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.07233 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Riegler_Stable_View_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Riegler_Stable_View_Synthesis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Deep Two-View Structure-From-Motion Revisited | Jianyuan Wang, Yiran Zhong, Yuchao Dai, Stan Birchfield, Kaihao Zhang, Nikolai Smolyanskiy, Hongdong Li | Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM. Existing deep learning-based approaches formulate the problem in ways that are fundamentally ill-posed, relying on training data to overcome the inherent difficulties. In contrast, we propose a return to the basics. We revisit the problem of deep two-view SfM by leveraging the well-posedness of the classic pipeline. Our method consists of 1) an optical flow estimation network that predicts dense correspondences between two frames; 2) a normalized pose estimation module that computes relative camera poses from the 2D optical flow correspondences, and 3) a scale-invariant depth estimation network that leverages epipolar geometry to reduce the search space, refine the dense correspondences, and estimate relative depth maps. Extensive experiments show that our method outperforms all state-of-the-art two-view SfM methods by a clear margin on KITTI depth, KITTI VO, MVS, Scenes11, and SUN3D datasets in both relative pose estimation and depth estimation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Deep_Two-View_Structure-From-Motion_Revisited_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Deep_Two-View_Structure-From-Motion_Revisited_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Deep_Two-View_Structure-From-Motion_Revisited_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Deep_Two-View_Structure-From-Motion_CVPR_2021_supplemental.zip | null |
Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes | Dmytro Kotovenko, Matthias Wright, Arthur Heimbrecht, Bjorn Ommer | There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism. Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input. We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kotovenko_Rethinking_Style_Transfer_From_Pixels_to_Parameterized_Brushstrokes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17185 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kotovenko_Rethinking_Style_Transfer_From_Pixels_to_Parameterized_Brushstrokes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kotovenko_Rethinking_Style_Transfer_From_Pixels_to_Parameterized_Brushstrokes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kotovenko_Rethinking_Style_Transfer_CVPR_2021_supplemental.pdf | null |
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation | Rui Gong, Yuhua Chen, Danda Pani Paudel, Yawei Li, Ajad Chhatkuli, Wen Li, Dengxin Dai, Luc Van Gool | Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains. In this work, we propose a principled meta-learning based approach to OCDA for semantic segmentation, MOCDA, by modeling the unlabeled target domain continuously. Our approach consists of four key steps. First, we cluster target domain into multiple sub-target domains by image styles, extracted in an unsupervised manner. Then, different sub-target domains are split into independent branches, for which batch normalization parameters are learnt to treat them independently. A meta-learner is thereafter deployed to learn to fuse sub-target domain-specific predictions, conditioned upon the style code. Meanwhile, we learn to online update the model by model-agnostic meta-learning (MAML) algorithm, thus to further improve generalization. We validate the benefits of our approach by extensive experiments on synthetic-to-real knowledge transfer benchmark, where we achieve the state-of-the-art performance in both compound and open domains. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_Cluster_Split_Fuse_and_Update_Meta-Learning_for_Open_Compound_Domain_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.08278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Cluster_Split_Fuse_and_Update_Meta-Learning_for_Open_Compound_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Cluster_Split_Fuse_and_Update_Meta-Learning_for_Open_Compound_Domain_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_Cluster_Split_Fuse_CVPR_2021_supplemental.pdf | null |
Beyond Short Clips: End-to-End Video-Level Learning With Collaborative Memories | Xitong Yang, Haoqi Fan, Lorenzo Torresani, Larry S. Davis, Heng Wang | The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label. We argue that a single clip may not have enough temporal coverage to exhibit the label to recognize, since video datasets are often weakly labeled with categorical information but without dense temporal annotations. Furthermore, optimizing the model over brief clips impedes its ability to learn long-term temporal dependencies. To overcome these limitations, we introduce a collaborative memory mechanism that encodes information across multiple sampled clips of a video at each training iteration. This enables the learning of long-range dependencies beyond a single clip. We explore different design choices for the collaborative memory to ease the optimization difficulties. Our proposed framework is end-to-end trainable and significantly improves the accuracy of video classification at a negligible computational overhead. Through extensive experiments, we demonstrate that our framework generalizes to different video architectures and tasks, outperforming the state of the art on both action recognition (e.g., Kinetics-400 & 700, Charades, Something-Something-V1) and action detection (e.g., AVA v2.1 & v2.2). | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Beyond_Short_Clips_End-to-End_Video-Level_Learning_With_Collaborative_Memories_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Beyond_Short_Clips_End-to-End_Video-Level_Learning_With_Collaborative_Memories_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Beyond_Short_Clips_End-to-End_Video-Level_Learning_With_Collaborative_Memories_CVPR_2021_paper.html | CVPR 2021 | null | null |
PointDSC: Robust Point Cloud Registration Using Deep Spatial Consistency | Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, Chiew-Lan Tai | Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_PointDSC_Robust_Point_Cloud_Registration_Using_Deep_Spatial_Consistency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05465 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_PointDSC_Robust_Point_Cloud_Registration_Using_Deep_Spatial_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_PointDSC_Robust_Point_Cloud_Registration_Using_Deep_Spatial_Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_PointDSC_Robust_Point_CVPR_2021_supplemental.pdf | null |
Task Programming: Learning Data Efficient Behavior Representations | Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona | Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation effort, we present TREBA: a method to learn annotation-sample efficient trajectory embedding for behavior analysis, based on multi-task self-supervised learning. The tasks in our method can be efficiently engineered by domain experts through a process we call "task programming", which uses programs to explicitly encode structured knowledge from domain experts. Total domain expert effort can be reduced by exchanging data annotation time for the construction of a small number of programmed tasks. We evaluate this trade-off using data from behavioral neuroscience, in which specialized domain knowledge is used to identify behaviors. We present experimental results in three datasets across two domains: mice and fruit flies. Using embeddings from TREBA, we reduce annotation burden by up to a factor of 10 without compromising accuracy compared to state-of-the-art features. Our results thus suggest that task programming and self-supervision can be an effective way to reduce annotation effort for domain experts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Task_Programming_Learning_Data_Efficient_Behavior_Representations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13917 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Task_Programming_Learning_Data_Efficient_Behavior_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Task_Programming_Learning_Data_Efficient_Behavior_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Task_Programming_Learning_CVPR_2021_supplemental.pdf | null |
ACRE: Abstract Causal REasoning Beyond Covariation | Chi Zhang, Baoxiong Jia, Mark Edmonds, Song-Chun Zhu, Yixin Zhu | Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in various settings despite its notorious difficulty. However, in contrast to the commonplace trait of human cognition is the lack of a diagnostic benchmark to measure causal induction for modern Artificial Intelligence (AI) systems. Therefore, in this work, we introduce the Abstract Causal REasoning (ACRE) dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario: direct, indirect, screening-off, and backward-blocking, intentionally going beyond the simple strategy of inducing causal relationships by covariation. By analyzing visual reasoning architectures on this testbed, we notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning. These deficiencies call for future research in models with a more comprehensive capability of causal induction. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_ACRE_Abstract_Causal_REasoning_Beyond_Covariation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ACRE_Abstract_Causal_REasoning_Beyond_Covariation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ACRE_Abstract_Causal_REasoning_Beyond_Covariation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_ACRE_Abstract_Causal_CVPR_2021_supplemental.pdf | null |
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain Decomposition | Jingwei Huang, Shan Huang, Mingwei Sun | We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast convergence. We implement a general and efficient LM solver on a deep learning framework by designing a new backward jacobian network to enable automatic sparse jacobian matrix computation. Furthermore, we introduce a stochastic domain decomposition approach that enables batched optimization and preserves convergence for large problems. We evaluate our method by solving bundle adjustment as a fundamental problem. Experiments show that our optimizer significantly outperforms the state-of-the-art solutions and existing deep learning solvers considering quality, efficiency, and memory. Our stochastic domain decomposition enables distributed optimization, consumes little memory and time, and achieves similar quality compared to a global solver. As a result, our solver effectively solves nonlinear least squares on an extremely large scale. We will make the code publicly available on publication. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_DeepLM_Large-Scale_Nonlinear_Least_Squares_on_Deep_Learning_Frameworks_Using_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_DeepLM_Large-Scale_Nonlinear_Least_Squares_on_Deep_Learning_Frameworks_Using_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_DeepLM_Large-Scale_Nonlinear_Least_Squares_on_Deep_Learning_Frameworks_Using_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_DeepLM_Large-Scale_Nonlinear_CVPR_2021_supplemental.pdf | null |
TDN: Temporal Difference Networks for Efficient Action Recognition | Limin Wang, Zhan Tong, Bin Ji, Gangshan Wu | Temporal modeling still remains challenging for action recognition in videos. To mitigate this issue, this paper presents a new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition. The core of our TDN is to devise an efficient temporal module (TDM) by explicitly leveraging a temporal difference operator, and systematically assess its effect on short-term and long-term motion modeling. To fully capture temporal information over the entire video, our TDN is established with a two-level difference modeling paradigm. Specifically, for local motion modeling, temporal difference over consecutive frames is used to supply 2D CNNs with finer motion pattern, while for global motion modeling, temporal difference across segments is incorporated to capture long-range structure for motion feature excitation. TDN provides a simple and principled temporal modeling framework and could be instantiated with the existing CNNs at a small extra computational cost. Our TDN presents a new state of the art on the Something-Something V1 & V2 datasets and is on par with the best performance on the Kinetics-400 dataset. In addition, we conduct in-depth ablation studies and plot the visualization results of our TDN, hopefully providing insightful analysis on temporal difference modeling. We release the code at https://github.com/MCG-NJU/TDN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_TDN_Temporal_Difference_Networks_for_Efficient_Action_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.10071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_TDN_Temporal_Difference_Networks_for_Efficient_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_TDN_Temporal_Difference_Networks_for_Efficient_Action_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_TDN_Temporal_Difference_CVPR_2021_supplemental.pdf | null |
LiBRe: A Practical Bayesian Approach to Adversarial Detection | Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu | Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples. Various adversarial defense strategies have been proposed to resolve this problem, but they typically demonstrate restricted practicability owing to unsurmountable compromise on universality, effectiveness, or efficiency. In this work, we propose a more practical approach, Lightweight Bayesian Refinement (LiBRe), in the spirit of leveraging Bayesian neural networks (BNNs) for adversarial detection. Empowered by the task and attack agnostic modeling under Bayes principle, LiBRe can endow a variety of pre-trained task-dependent DNNs with the ability of defending heterogeneous adversarial attacks at a low cost. We develop and integrate advanced learning techniques to make LiBRe appropriate for adversarial detection. Concretely, we build the few-layer deep ensemble variational and adopt the pre-training & fine-tuning workflow to boost the effectiveness and efficiency of LiBRe. We further provide a novel insight to realise adversarial detection-oriented uncertainty quantification without inefficiently crafting adversarial examples during training. Extensive empirical studies covering a wide range of scenarios verify the practicability of LiBRe. We also conduct thorough ablation studies to evidence the superiority of our modeling and learning strategies. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_LiBRe_A_Practical_Bayesian_Approach_to_Adversarial_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LiBRe_A_Practical_Bayesian_Approach_to_Adversarial_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LiBRe_A_Practical_Bayesian_Approach_to_Adversarial_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Deng_LiBRe_A_Practical_CVPR_2021_supplemental.pdf | null |
ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes | Hao Su, Jianwei Niu, Xuefeng Liu, Qingfeng Li, Ji Wan, Mingliang Xu, Tao Ren | Quick Response (QR) code is one of the most worldwide used two-dimensional codes. Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works typically beatify QR codes in a single style due to the fixed generation algorithms, which is improvable in personalization and diversification. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end network ACN (ArtCode-Net) to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust. To address the challenge that preserving the scanning-robustness after giving such codes style elements, we further propose the Sampling-Simulation layer, the module-based code loss, and a competition mechanism to improve the performances of ACN. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application. | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.html | CVPR 2021 | null | null |
Self-Supervised Pillar Motion Learning for Autonomous Driving | Chenxu Luo, Xiaodong Yang, Alan Yuille | Autonomous driving can benefit from motion behavior comprehension when interacting with diverse traffic participants in highly dynamic environments. Recently, there has been a growing interest in estimating class-agnostic motion directly from point clouds. Current motion estimation methods usually require vast amount of annotated training data from self-driving scenes. However, manually labeling point clouds is notoriously difficult, error-prone and time-consuming. In this paper, we seek to answer the research question of whether the abundant unlabeled data collections can be utilized for accurate and efficient motion learning. To this end, we propose a learning framework that leverages free supervisory signals from point clouds and paired camera images to estimate motion purely via self-supervision. Our model involves a point cloud based structural consistency augmented with probabilistic motion masking as well as a cross-sensor motion regularization to realize the desired self-supervision. Experiments reveal that our approach performs competitively to supervised methods, and achieves the state-of-the-art result when combining our self-supervised model with supervised fine-tuning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Self-Supervised_Pillar_Motion_Learning_for_Autonomous_Driving_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08683 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Self-Supervised_Pillar_Motion_Learning_for_Autonomous_Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Self-Supervised_Pillar_Motion_Learning_for_Autonomous_Driving_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Self-Supervised_Pillar_Motion_CVPR_2021_supplemental.pdf | null |
Quantum Permutation Synchronization | Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas J. Guibas | We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the binary nature of the problem, ensuring that the result is a set of permutations requires extra care. Hence, we: (i) show how to insert permutation constraints into a QUBO problem and (ii) solve the constrained QUBO problem on the current generation of the adiabatic quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global optimality with high probability while sampling the energy landscape to yield confidence estimates. Our proof-of-concepts realization on the adiabatic D-Wave computer demonstrates that quantum machines offer a promising way to solve the prevalent yet difficult synchronization problems. | https://openaccess.thecvf.com/content/CVPR2021/papers/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.07755 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Birdal_Quantum_Permutation_Synchronization_CVPR_2021_supplemental.pdf | null |
QAIR: Practical Query-Efficient Black-Box Attacks for Image Retrieval | Xiaodan Li, Jinfeng Li, Yuefeng Chen, Shaokai Ye, Yuan He, Shuhui Wang, Hang Su, Hui Xue | We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query access to the top-k ranked unlabeled images from the database. Compared with query attacks in image classification, which produce adversaries according to the returned labels or confidence score, the challenge becomes even more prominent due to the difficulty in quantifying the attack effectiveness on the partial retrieved list. In this paper, we make the first attempt in Query-based Attack against Image Retrieval (QAIR), to completely subvert the top-k retrieval results. Specifically, a new relevance-based loss is designed to quantify the attack effects by measuring the set similarity on the top-k retrieval results before and after attacks and guide the gradient optimization. To further boost the attack efficiency, a recursive model stealing method is proposed to acquire transferable priors on the target model and generate the prior-guided gradients. Comprehensive experiments show that the proposed attack achieves a high attack success rate with few queries against the image retrieval systems under the black-box setting. The attack evaluations on real-world visual search engine show that it successfully deceives a commercial system such as Bing Visual Search with 98% attack success rate by only 33 queries on average. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_QAIR_Practical_Query-Efficient_Black-Box_Attacks_for_Image_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02927 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_QAIR_Practical_Query-Efficient_Black-Box_Attacks_for_Image_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_QAIR_Practical_Query-Efficient_Black-Box_Attacks_for_Image_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_QAIR_Practical_Query-Efficient_CVPR_2021_supplemental.zip | null |
MagFace: A Universal Representation for Face Recognition and Quality Assessment | Qiang Meng, Shichao Zhao, Zhida Huang, Feng Zhou | The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude before normalization can measure with the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a well-structured within-class feature distributions by pushing easy samples to class centers while pushing hard samples away. This prevents models from overfitting on noisy low-quality samples and improves face recognition in the wild. Extensive experiments conducted on face recognition, quality assessments as well as clustering have demonstrated the effectiveness of MagFace over state-of-the-arts. The code is available at https://github.com/IrvingMeng/MagFace. | https://openaccess.thecvf.com/content/CVPR2021/papers/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meng_MagFace_A_Universal_CVPR_2021_supplemental.pdf | null |
Wasserstein Barycenter for Multi-Source Domain Adaptation | Eduardo Fernandes Montesuma, Fred Maurice Ngole Mboula | Multi-source domain adaptation is a key technique that allows a model to be trained on data coming from various probability distribution. To overcome the challenges posed by this learning scenario, we propose a method for constructing an intermediate domain between sources and target domain, the Wasserstein Barycenter Transport (WBT). This method relies on the barycenter on Wasserstein spaces for aggregating the source probability distributions. Once the sources have been aggregated, they are transported to the target domain using standard Optimal Transport for Domain Adaptation framework. Additionally, we revisit previous single-source domain adaptation tasks in the context of multi-source scenario. In particular, we apply our algorithm to object and face recognition datasets. Moreover, to diversify the range of applications, we also examine the tasks of music genre recognition and music-speech discrimination. The experiments show that our method has similar performance with the existing state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Montesuma_Wasserstein_Barycenter_for_CVPR_2021_supplemental.pdf | null |
Unsupervised Hyperbolic Metric Learning | Jiexi Yan, Lei Luo, Cheng Deng, Heng Huang | Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning. Following the paradigm in supervised manner, most existing unsupervised metric learning approaches mainly focus on binary similarity in Euclidean space. However, these methods cannot achieve promising performance in many practical applications, where the manual information is lacking and data exhibits non-Euclidean latent anatomy. To address this limitation, we propose an Unsupervised Hyperbolic Metric Learning method with Hierarchical Similarity. It considers the natural hierarchies of data by taking advantage of Hyperbolic metric learning and hierarchical clustering, which can effectively excavate richer similarity information beyond binary in modeling. More importantly, we design a new loss function to capture the hierarchical similarity among samples to enhance the stability of the proposed method. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current unsupervised deep metric learning approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Unsupervised_Hyperbolic_Metric_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Unsupervised_Hyperbolic_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Unsupervised_Hyperbolic_Metric_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Improving Sign Language Translation With Monolingual Data by Sign Back-Translation | Hao Zhou, Wengang Zhou, Weizhen Qi, Junfu Pu, Houqiang Li | Despite existing pioneering works on sign language translation (SLT), there is a non-trivial obstacle, i.e., the limited quantity of parallel sign-text data. To tackle this parallel data bottleneck, we propose a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into SLT training. With a text-to-gloss translation model, we first back-translate the monolingual text to its gloss sequence. Then, the paired sign sequence is generated by splicing pieces from an estimated gloss-to-sign bank at the feature level. Finally, the synthetic parallel data serves as a strong supplement for the end-to-end training of the encoder-decoder SLT framework. To promote the SLT research, we further contribute CSL-Daily, a large-scale continuous SLT dataset. It provides both spoken language translations and gloss-level annotations. The topic revolves around people's daily lives (e.g., travel, shopping, medical care), the most likely SLT application scenario. Extensive experimental results and analysis of SLT methods are reported on CSL-Daily. With the proposed sign back-translation method, we obtain a substantial improvement over previous state-of-the-art SLT methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.12397 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Background Splitting: Finding Rare Classes in a Sea of Background | Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian | We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dataset. We find that state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in this regime. Our solution is to split the large, visually diverse background into many smaller, visually similar categories during training. We implement this idea by extending an image classification model with an additional auxiliary loss that learns to mimic the predictions of a pre-existing classification model on the training set. The auxiliary loss requires no additional human labels and regularizes feature learning in the shared network trunk by forcing the model to discriminate between auxiliary categories for all training set examples, including those belonging to the monolithic background of the main rare category classification task. To evaluate our method we contribute modified versions of the iNaturalist and Places365 datasets where only a small subset of rare category labels are available during training (all other images are labeled as background). By jointly learning to recognize both the selected rare categories and auxiliary categories, our approach yields models that perform 8.3 mAP points higher than state-of-the-art imbalanced learning baselines when 98.30% of the data is background, and up to 42.3 mAP points higher than fine-tuning baselines when 99.98% of the data is background. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mullapudi_Background_Splitting_Finding_Rare_Classes_in_a_Sea_of_Background_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.12873 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mullapudi_Background_Splitting_Finding_Rare_Classes_in_a_Sea_of_Background_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mullapudi_Background_Splitting_Finding_Rare_Classes_in_a_Sea_of_Background_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mullapudi_Background_Splitting_Finding_CVPR_2021_supplemental.pdf | null |
Adaptive Convolutions for Structure-Aware Style Transfer | Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Markus Gross, Derek Bradley | Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properties of style features to a content image, and can transfer a large number of styles in real time. However, AdaIN is a global operation; thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive Convolutions (AdaConv), a generic extension of AdaIN, to allow for the simultaneous transfer of both statistical and structural styles in real time. Apart from style transfer, our method can also be readily extended to style-based image generation, and other tasks where AdaIN has already been adopted. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chandran_Adaptive_Convolutions_for_Structure-Aware_Style_Transfer_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chandran_Adaptive_Convolutions_for_Structure-Aware_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chandran_Adaptive_Convolutions_for_Structure-Aware_Style_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chandran_Adaptive_Convolutions_for_CVPR_2021_supplemental.zip | null |
Few-Shot Incremental Learning With Continually Evolved Classifiers | Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, Yinghui Xu | Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge of old classes. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. Moreover, as training data come in sequence in FSCIL, the learned classifier can only provide discriminative information in individual sessions, while FSCIL requires all classes to be involved for evaluation. In this paper, we address the FSCIL problem from two aspects. First, we adopt a simple but effective decoupled learning strategy of representations and classifiers that only the classifiers are updated in each incremental session, which avoids knowledge forgetting in the representations. By doing so, we demonstrate that a pre-trained backbone plus a non-parametric class mean classifier can beat state-of-the-art methods. Second, to make the classifiers learned on individual sessions applicable to all classes, we propose a Continually Evolved Classifier (CEC) that employs a graph model to propagate context information between classifiers for adaptation. To enable the learning of CEC, we design a pseudo incremental learning paradigm that episodically constructs a pseudo incremental learning task to optimize the graph parameters by sampling data from the base dataset. Experiments on three popular benchmark datasets, including CIFAR100, miniImageNet, and Caltech-USCD Birds-200-2011 (CUB200), show that our method significantly outperforms the baselines and sets new state-of-the-art results with remarkable advantages. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Few-Shot_Incremental_Learning_With_Continually_Evolved_Classifiers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Few-Shot_Incremental_Learning_CVPR_2021_supplemental.pdf | null |
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions | Junbin Xiao, Xindi Shang, Angela Yao, Tat-Seng Chua | We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting at causal action reasoning, temporal action reasoning and common scene comprehension. Through extensive analysis of baselines and established VideoQA techniques, we find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning. Furthermore, the models that are effective on multi-choice QA, when adapted to open-ended QA, still struggle in generalizing the answers. This raises doubt on the ability of these models to reason and highlights possibilities for improvement. With detailed results for different question types and heuristic observations for future works, we hope NExT-QA will guide the next generation of VQA research to go beyond superficial description towards a deeper understanding of videos. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_NExT-QA_Next_Phase_of_Question-Answering_to_Explaining_Temporal_Actions_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_NExT-QA_Next_Phase_of_Question-Answering_to_Explaining_Temporal_Actions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_NExT-QA_Next_Phase_of_Question-Answering_to_Explaining_Temporal_Actions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiao_NExT-QA_Next_Phase_CVPR_2021_supplemental.pdf | null |
LayoutGMN: Neural Graph Matching for Structural Layout Similarity | Akshay Gadi Patil, Manyi Li, Matthew Fisher, Manolis Savva, Hao Zhang | We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements. | https://openaccess.thecvf.com/content/CVPR2021/papers/Patil_LayoutGMN_Neural_Graph_Matching_for_Structural_Layout_Similarity_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06547 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Patil_LayoutGMN_Neural_Graph_Matching_for_Structural_Layout_Similarity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Patil_LayoutGMN_Neural_Graph_Matching_for_Structural_Layout_Similarity_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Patil_LayoutGMN_Neural_Graph_CVPR_2021_supplemental.pdf | null |
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search | Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li | Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset and code will be available at Mindspore and VEGA. | https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_TransNAS-Bench-101_Improving_Transferability_and_Generalizability_of_Cross-Task_Neural_Architecture_Search_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_TransNAS-Bench-101_Improving_Transferability_and_Generalizability_of_Cross-Task_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_TransNAS-Bench-101_Improving_Transferability_and_Generalizability_of_Cross-Task_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duan_TransNAS-Bench-101_Improving_Transferability_CVPR_2021_supplemental.pdf | null |
ArtEmis: Affective Language for Visual Art | Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas J. Guibas | We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice. As we demonstrate below, this leads to a rich set of signals for both the objective content and the affective impact of an image, creating associations with abstract concepts (e.g., "freedom" or "love"), or references that go beyond what is directly visible, including visual similes and metaphors, or subjective references to personal experiences. We focus on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. Our dataset, termed ArtEmis, contains 455K emotion attributions and explanations from humans, on 80K artworks from WikiArt. Building on this data, we train and demonstrate a series of captioning systems capable of expressing and explaining emotions from visual stimuli. Remarkably, the captions produced by these systems often succeed in reflecting the semantic and abstract content of the image, going well beyond systems trained on existing datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Achlioptas_ArtEmis_Affective_Language_for_Visual_Art_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.07396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Achlioptas_ArtEmis_Affective_Language_for_Visual_Art_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Achlioptas_ArtEmis_Affective_Language_for_Visual_Art_CVPR_2021_paper.html | CVPR 2021 | null | null |
Sketch, Ground, and Refine: Top-Down Dense Video Captioning | Chaorui Deng, Shizhe Chen, Da Chen, Yuan He, Qi Wu | The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the definitions of events are diverse which could be as simple as a single action or as complex as a set of events, depending on different semantic contexts. Therefore, directly detecting events based on video information is ill-defined and hurts the coherency and accuracy of generated dense captions. In this work, we reverse the predominant "detect-then-describe" fashion, proposing a top-down way to first generate paragraphs from a global view and then ground each event description to a video segment for detailed refinement. It is formulated as a Sketch, Ground, and Refine process (SGR). The sketch stage first generates a coarse-grained multi-sentence paragraph to describe the whole video, where each sentence is treated as an event and gets localised in the grounding stage. In the refining stage, we improve captioning quality via refinement-enhanced training and dual-path cross attention on both coarse-grained event captions and aligned event segments. The updated event caption can further adjust its segment boundaries. Our SGR model outperforms state-of-the-art methods on ActivityNet Captioning benchmark under traditional and story-oriented dense caption evaluations. Code will be released at github.com/bearcatt/SGR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Sketch_Ground_and_Refine_Top-Down_Dense_Video_Captioning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Normal Dynamics in Videos With Meta Prototype Network | Hui Lv, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, Jian Yang | Frame reconstruction (current or future frames) based on Auto-Encoder (AE) is a popular method for video anomaly detection. With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones. Previous methods introduced the memory bank into AE, for encoding diverse normal patterns across the training videos. However, they are memory-consuming and cannot cope with unseen new scenarios in the training data. In this work, we propose a dynamic prototype unit (DPU) to encode the normal dynamics as prototypes in real time, free from extra memory cost. In addition, we introduce meta-learning to our DPU to form a novel few-shot normalcy learner, namely Meta-Prototype Unit (MPU). It enables the fast adaption capability on new scenes by only consuming a few iterations of update. Extensive experiments are conducted on various benchmarks. The superior performance over the state-of-the-art demonstrates the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Learning_Normal_Dynamics_in_Videos_With_Meta_Prototype_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06689 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Normal_Dynamics_in_Videos_With_Meta_Prototype_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Normal_Dynamics_in_Videos_With_Meta_Prototype_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Learning_Normal_Dynamics_CVPR_2021_supplemental.pdf | null |
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition | Yifan Zhao, Ke Yan, Feiyue Huang, Jia Li | Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify discriminative part regions with attention mechanisms. Besides its unstable performance under complex backgrounds, the intrinsic interrelationship between different semantic features is less explored. Toward this end, we propose an effective graph-based relation discovery approach to build a contextual understanding of high-order relationships. In our approach, a high-dimensional feature bank is first formed and jointly regularized with semantic- and positional-aware high-order constraints, endowing rich attributes to feature representations. Second, to overcome the high-dimension curse, we propose a graph-based semantic grouping strategy to embed this high-order tensor bank into a low-dimensional space. Meanwhile, a group-wise learning strategy is proposed to regularize the features focusing on the cluster embedding center. With the collaborative learning of three modules, our module is able to grasp the stronger contextual details of fine-grained objects. Experimental evidence demonstrates our approach achieves new state-of-the-art on 4 widely-used fine-grained object recognition benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Graph-Based_High-Order_Relation_Discovery_for_Fine-Grained_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Graph-Based_High-Order_Relation_Discovery_for_Fine-Grained_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Graph-Based_High-Order_Relation_Discovery_for_Fine-Grained_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
Normal Integration via Inverse Plane Fitting With Minimum Point-to-Plane Distance | Xu Cao, Boxin Shi, Fumio Okura, Yasuyuki Matsushita | This paper presents a surface normal integration method that solves an inverse problem of local plane fitting. Surface reconstruction from normal maps is essential in photometric shape reconstruction. To this end, we formulate normal integration in the camera coordinates and jointly solve for 3D point positions and local plane displacements. Unlike existing methods that consider the vertical distances between 3D points, we minimize the sum of squared point-to-plane distances. Our method can deal with both orthographic or perspective normal maps with arbitrary boundaries. Compared to existing normal integration methods, our method avoids the checkerboard artifact and performs more robustly against natural boundaries, sharp features, and outliers. We further provide a geometric analysis of the source of artifacts that appear in previous methods based on our plane fitting formulation. Experimental results on analytically computed, synthetic, and real-world surfaces show that our method yields accurate and stable reconstruction for both orthographic and perspective normal maps. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_Normal_Integration_via_Inverse_Plane_Fitting_With_Minimum_Point-to-Plane_Distance_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Normal_Integration_via_Inverse_Plane_Fitting_With_Minimum_Point-to-Plane_Distance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Normal_Integration_via_Inverse_Plane_Fitting_With_Minimum_Point-to-Plane_Distance_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cao_Normal_Integration_via_CVPR_2021_supplemental.pdf | null |
NPAS: A Compiler-Aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration | Zhengang Li, Geng Yuan, Wei Niu, Pu Zhao, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Bin Ren, Yanzhi Wang, Xue Lin | With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently, and do not fully consider compiler-level optimizations which is a must-do for mobile acceleration. In this work, we first propose (i) a general category of fine-grained structured pruning applicable to various DNN layers, and (ii) a comprehensive, compiler automatic code generation framework supporting different DNNs and different pruning schemes, which bridge the gap of model compression and NAS. We further propose NPAS, a compiler-aware unified network pruning and architecture search. To deal with large search space, we propose a meta-modeling procedure based on reinforcement learning with fast evaluation and Bayesian optimization, ensuring the total number of training epochs comparable with representative NAS frameworks. Our framework achieves 6.7ms, 5.9ms, and 3.9ms ImageNet inference times with 78.2%, 75% (MobileNet-V3 level), and 71% (MobileNet-V2 level) Top-1 accuracy respectively on an off-the-shelf mobile phone, consistently outperforming prior work. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_NPAS_A_Compiler-Aware_Framework_of_Unified_Network_Pruning_and_Architecture_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00596 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_NPAS_A_Compiler-Aware_Framework_of_Unified_Network_Pruning_and_Architecture_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_NPAS_A_Compiler-Aware_Framework_of_Unified_Network_Pruning_and_Architecture_CVPR_2021_paper.html | CVPR 2021 | null | null |
Spatial Feature Calibration and Temporal Fusion for Effective One-Stage Video Instance Segmentation | Minghan Li, Shuai Li, Lida Li, Lei Zhang | Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth bounding boxes, reducing the mask sensitivity to spatial location. Second, a video is directly divided into individual frames for frame-level instance segmentation, ignoring the temporal correlation between adjacent frames. To address these issues, we propose a simple yet effective one-stage video instance segmentation framework by spatial calibration and temporal fusion, namely STMask. To ensure spatial feature calibration with ground-truth bounding boxes, we first predict regressed bounding boxes around ground-truth bounding boxes, and extract features from them for frame-level instance segmentation. To further explore temporal correlation among video frames, we aggregate a temporal fusion module to infer instance masks from each frame to its adjacent frames, which helps our framework to handle challenging videos such as motion blur, partial occlusion and unusual object-to-camera poses. Experiments on the YouTube-VIS valid set show that the proposed STMask with ResNet-50/-101 backbone obtains 33.5 % / 36.8 % mask AP, while achieving 28.6 / 23.4 FPS on video instance segmentation. The code is released online https://github.com/MinghanLi/STMask. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05606 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Spatial_Feature_Calibration_CVPR_2021_supplemental.pdf | null |
Learning Asynchronous and Sparse Human-Object Interaction in Videos | Romero Morais, Vuong Le, Svetha Venkatesh, Truyen Tran | Human activities can be learned from video. With effective modeling it is possible to discover not only the action labels but also the temporal structure of the activities, such as the progression of the sub-activities. Automatically recognizing such structure from raw video signal is a new capability that promises authentic modeling and successful recognition of human-object interactions. Toward this goal, we introduce Asynchronous-Sparse Interaction Graph Networks (ASSIGN), a recurrent graph network that is able to automatically detect the structure of interaction events associated with entities in a video scene. ASSIGN pioneers learning of autonomous behavior of video entities including their dynamic structure and their interaction with the coexisting neighbors. Entities' lives in our model are asynchronous to those of others therefore more flexible in adapting to complex scenarios. Their interactions are sparse in time hence more faithful to the true underlying nature and more robust in inference and learning. ASSIGN is tested on human-object interaction recognition and shows superior performance in segmenting and labeling of human sub-activities and object affordances from raw videos. The native ability of ASSIGN in discovering temporal structure also eliminates the dependence on external segmentation that was previously mandatory for this task. | https://openaccess.thecvf.com/content/CVPR2021/papers/Morais_Learning_Asynchronous_and_Sparse_Human-Object_Interaction_in_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02758 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Morais_Learning_Asynchronous_and_Sparse_Human-Object_Interaction_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Morais_Learning_Asynchronous_and_Sparse_Human-Object_Interaction_in_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morais_Learning_Asynchronous_and_CVPR_2021_supplemental.pdf | null |
Single Image Reflection Removal With Absorption Effect | Qian Zheng, Boxin Shi, Jinnan Chen, Xudong Jiang, Ling-Yu Duan, Alex C. Kot | In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitly takes the absorption effect into account. The first step estimates the absorption effect from a reflection-contaminated image, while the second step recovers the transmission image by taking a reflection-contaminated image and the estimated absorption effect as the input. Experimental results on four public datasets show that our two-step solution not only successfully removes reflection artifact, but also faithfully restores the intensity distortion caused by the absorption effect. Our ablation studies further demonstrate that our method achieves superior performance on the recovery of overall intensity and has good model generalization capacity. The code is available at https://github.com/q-zh/absorption. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Single_Image_Reflection_Removal_With_Absorption_Effect_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Single_Image_Reflection_CVPR_2021_supplemental.pdf | null |
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking | Minghao Chen, Jianlong Fu, Haibin Ling | Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired by the merits of model ensemble, we propose to search for multiple diverse models simultaneously as an alternative way to find powerful models. Searching for ensembles is non-trivial and has two key challenges: enlarged search space and potentially more complexity for the searched model. In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges. For the first challenge, we introduce a novel diversity-based metric to guide search space shrinking, considering both the potentiality and diversity of candidate operators. For the second challenge, we enable a new search dimension to learn layer sharing among different models for efficiency purposes. The experiments on ImageNet clearly demonstrate that our solution can improve the supernet's capacity of ranking ensemble architectures, and further lead to better search results. The discovered architectures achieve superior performance compared with state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned settings. Moreover, we evaluate the generalization ability and robustness of our searched architecture on the COCO detection benchmark and achieve a 3.1% improvement on AP compared with MobileNetV3. Codes and models are available here. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_One-Shot_Neural_Ensemble_Architecture_Search_by_Diversity-Guided_Search_Space_Shrinking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_One-Shot_Neural_Ensemble_Architecture_Search_by_Diversity-Guided_Search_Space_Shrinking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_One-Shot_Neural_Ensemble_Architecture_Search_by_Diversity-Guided_Search_Space_Shrinking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_One-Shot_Neural_Ensemble_CVPR_2021_supplemental.pdf | null |
Disentangled Cycle Consistency for Highly-Realistic Virtual Try-On | Chongjian Ge, Yibing Song, Yuying Ge, Han Yang, Wei Liu, Ping Luo | Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation networks to reconstruct the input image in a self-supervised manner. However, existing methods do not differentiate clothing and non-clothing regions. A straightforward generation impedes the virtual try-on quality because of the heavily coupled image contents. In this paper, we propose a Disentangled Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce highly-realistic try-on images by disentangling important components of virtual try-on including clothes warping, skin synthesis, and image composition. Moreover, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning. Extensive experiments on challenging benchmarks show that DCTON outperforms state-of-the-art approaches favorably. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.09479 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Disentangled_Cycle_Consistency_for_Highly-Realistic_Virtual_Try-On_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_Disentangled_Cycle_Consistency_CVPR_2021_supplemental.pdf | null |
M3DSSD: Monocular 3D Single Stage Object Detector | Shujie Luo, Hang Dai, Ling Shao, Yong Ding | In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores. In the second step, the center alignment is used to align the features at 2D/3D centers. Further, it is often difficult to learn global information and capture long-range relationships, which are important for the depth prediction of objects. Therefore, we propose a novel asymmetric non-local attention block with multi-scale sampling to extract depth-wise features. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITTI dataset, in both 3D object detection and bird's eye view tasks. The code is released at https://github.com/mumianyuxin/M3DSSD. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_M3DSSD_Monocular_3D_Single_Stage_Object_Detector_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13164 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_M3DSSD_Monocular_3D_Single_Stage_Object_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_M3DSSD_Monocular_3D_Single_Stage_Object_Detector_CVPR_2021_paper.html | CVPR 2021 | null | null |
Structure-Aware Face Clustering on a Large-Scale Graph With 107 Nodes | Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, Jie Zhou | Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are difficult to grasp the whole graph structure information and usually take a long time for inference. Previous approaches fail to tackle these two challenges simultaneously. To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method. Specifically, we design a structure-preserved subgraph sampling strategy to explore the power of large-scale training data, which can increase the training data scale from 10^5 to 10^7. During inference, the STAR-FC performs efficient full-graph clustering with two steps: graph parsing and graph refinement. And the concept of node intimacy is introduced in the second step to mine the local structural information. The STAR-FC gets 91.97 pairwise F-score on partial MS1M within 310s which surpasses the state-of-the-arts. Furthermore, we are the first to train on very large-scale graph with 20M nodes, and achieve superior inference results on 12M testing data. Overall, as a simple and effective method, the proposed STAR-FC provides a strong baseline for large-scale face clustering. Code is available at https://sstzal.github.io/STAR-FC/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Structure-Aware_Face_Clustering_CVPR_2021_supplemental.pdf | null |
Objects Are Different: Flexible Monocular 3D Object Detection | Yunpeng Zhang, Jiwen Lu, Jie Zhou | The precise localization of 3D objects from a single image without depth information is a highly challenging problem. Most existing methods adopt the same approach for all objects regardless of their diverse distributions, leading to limited performance especially for truncated objects. In this paper, we propose a flexible framework for monocular 3D object detection which explicitly decouples the truncated objects and adaptively combines multiple approaches for object depth estimation. Specifically, we decouple the edge of the feature map for predicting long-tail truncated objects so that the optimization of normal objects is not influenced. Furthermore, we formulate the object depth estimation as an uncertainty-guided ensemble of directly regressed object depth and solved depths from different groups of keypoints. Experiments demonstrate that our method outperforms the state-of-the-art method by relatively 27% for moderate level and 30% for hard level in the test set of KITTI benchmark while maintaining real-time efficiency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Objects_Are_Different_Flexible_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02323 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Objects_Are_Different_Flexible_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Objects_Are_Different_Flexible_Monocular_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification | Oren Nuriel, Sagie Benaim, Lior Wolf | Recent work has shown that convolutional neural network classifiers overly rely on texture at the expense of shape cues. We make a similar but different distinction between shape and local image cues, on the one hand, and global image statistics, on the other. Our method, called Permuted Adaptive Instance Normalization (pAdaIN), reduces the representation of global statistics in the hidden layers of image classifiers. pAdaIN samples a random permutation p that rearranges the samples in a given batch. Adaptive Instance Normalization (AdaIN) is then applied between the activations of each (non-permuted) sample i and the corresponding activations of the sample p(i), thus swapping statistics between the samples of the batch. Since the global image statistics are distorted, this swapping procedure causes the network to rely on cues, such as shape or texture. By choosing the random permutation with probability p and the identity permutation otherwise, one can control the effect's strength. With the correct choice of p, fixed apriori for all experiments and selected without considering test data, our method consistently outperforms baselines in multiple settings. In image classification, our method improves on both CIFAR100 and ImageNet using multiple architectures. In the setting of robustness, our method improves on both ImageNet-C and Cifar-100-C for multiple architectures. In the setting of domain adaptation and domain generalization, our method achieves state of the art results on the transfer learning task from GTAV to Cityscapes and on the PACS benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.05785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nuriel_Permuted_AdaIN_Reducing_the_Bias_Towards_Global_Statistics_in_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
Pixel Codec Avatars | Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De la Torre, Yaser Sheikh | Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances. In this work, we present the Pixel Codec Avatars (PiCA): a deep generative model of 3D human faces that achieves state of the art reconstruction performance while being computationally efficient and adaptive to the rendering conditions during execution. Our model combines two core ideas: (1) a fully convolutional architecture for decoding spatially varying features, and (2) a rendering-adaptive per-pixel decoder. Both techniques are integrated via a dense surface representation that is learned in a weakly-supervised manner from low-topology mesh tracking over training images. We demonstrate that PiCA improves reconstruction over existing techniques across testing expressions and views on persons of different gender and skin tone. Importantly, we show that the PiCA model is much smaller than the state-of-art baseline model, and makes multi-person telecommunicaiton possible: on a single Oculus Quest 2 mobile VR headset, 5 avatars are rendered in realtime in the same scene. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Pixel_Codec_Avatars_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Pixel_Codec_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Pixel_Codec_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Pixel_Codec_Avatars_CVPR_2021_supplemental.zip | null |
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification | Zijian Hu, Zhengyu Yang, Xuefeng Hu, Ram Nevatia | A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information not only from labeled data but also from a large amount of unlabeled data. Recent works have developed significant improvements by exploring the consistency constrain between differently augmented labeled and unlabeled data. Following this path, we propose a novel unsupervised objective that focuses on the less studied relationship between the high confidence unlabeled data that are similar to each other. The new proposed Pair Loss minimizes the statistical distance between high confidence pseudo labels with similarity above a certain threshold. Combining the Pair Loss with the techniques developed by the MixMatch family, our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par with the state-of-the-art methods on CIFAR-10 and SVHN. Furthermore, SimPLE also outperforms the state-of-the-art methods in the transfer learning setting, where models are initialized by the weights pre-trained on ImageNet or DomainNet-Real. The code is available at github.com/zijian-hu/SimPLE. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_SimPLE_Similar_Pseudo_Label_Exploitation_for_Semi-Supervised_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16725 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_SimPLE_Similar_Pseudo_Label_Exploitation_for_Semi-Supervised_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_SimPLE_Similar_Pseudo_Label_Exploitation_for_Semi-Supervised_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_SimPLE_Similar_Pseudo_CVPR_2021_supplemental.pdf | null |
Context-Aware Layout to Image Generation With Enhanced Object Appearance | Sen He, Wentong Liao, Michael Ying Yang, Yongxin Yang, Yi-Zhe Song, Bodo Rosenhahn, Tao Xiang | A layout to image (L2I) generation model aims to generate a complicated image containing multiple objects (things) against natural background (stuff), conditioned on a given layout. Built upon the recent advances in generative adversarial networks (GANs), recent L2I models have made great progress. However, a close inspection of their generated images reveals two major limitations: (1) the object-to-object as well as object-to-stuff relations are often broken and (2) each object's appearance is typically distorted lacking the key defining characteristics associated with the object class. We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators. To address these limitations, two new modules are proposed in this work. First, a contextual feature transformation module is introduced in the generator to ensure that the generated feature encoding of either object or stuff is aware of other co-existing objects/stuff in the scene. Second, instead of feeding location-insensitive image features to the discriminator, we use the Gram matrix computed from the feature maps of the generated object images to preserve location-sensitive information, resulting in much enhanced object appearance. Extensive experiments show that the proposed method achieves state-of-the-art performance on the COCO-Thing-Stuff and Visual Genome benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Context-Aware_Layout_to_Image_Generation_With_Enhanced_Object_Appearance_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11897 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_Context-Aware_Layout_to_Image_Generation_With_Enhanced_Object_Appearance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_Context-Aware_Layout_to_Image_Generation_With_Enhanced_Object_Appearance_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis | Yi Liu, Xiaoyang Huo, Tianyi Chen, Xiangping Zeng, Si Wu, Zhiwen Yu, Hau-San Wong | Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available. Learning precise class semantics is crucial for class-conditional image synthesis with limited supervision. Toward this end, we propose a semi-supervised Generative Adversarial Network with a Mask-Embedded Discriminator, which is referred to as MED-GAN. By incorporating a mask embedding module, the discriminator features are associated with spatial information, such that the focus of the discriminator can be limited in the specified regions when distinguishing between real and synthesized images. A generator is enforced to synthesize the instances holding more precise class semantics in order to deceive the enhanced discriminator. Also benefiting from mask embedding, region-based semantic regularization is imposed on the discriminator feature space, and the degree of separation between real and fake classes and among object categories can thus be increased. This eventually improves class-conditional distribution matching between real and synthesized data. In the experiments, the superior performance of MED-GAN demonstrates the effectiveness of mask embedding and associated regularizers in facilitating SSGL. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Mask-Embedded_Discriminator_With_Region-Based_Semantic_Regularization_for_Semi-Supervised_Class-Conditional_Image_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Mask-Embedded_Discriminator_With_Region-Based_Semantic_Regularization_for_Semi-Supervised_Class-Conditional_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Mask-Embedded_Discriminator_With_Region-Based_Semantic_Regularization_for_Semi-Supervised_Class-Conditional_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
LEAP: Learning Articulated Occupancy of People | Marko Mihajlovic, Yan Zhang, Michael J. Black, Siyu Tang | Substantial progress has been made on modeling rigid 3D objects using deep implicit representations. Yet, extending these methods to learn neural models of human shape is still in its infancy. Human bodies are complex and the key challenge is to learn a representation that generalizes such that it can express body shape deformations for unseen subjects in unseen, highly-articulated, poses. To address this challenge, we introduce LEAP (LEarning Articulated occupancy of People), a novel neural occupancy representation of the human body. Given a set of bone transformations (i.e. joint locations and rotations) and a query point in space, LEAP first maps the query point to a canonical space via learned linear blend skinning (LBS) functions and then efficiently queries the occupancy value via an occupancy network that models accurate identity- and pose-dependent deformations in the canonical space. Experiments show that our canonicalized occupancy estimation with the learned LBS functions greatly improves the generalization capability of the learned occupancy representation across various human shapes and poses, outperforming existing solutions in all settings. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mihajlovic_LEAP_Learning_Articulated_Occupancy_of_People_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06849 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mihajlovic_LEAP_Learning_Articulated_Occupancy_of_People_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mihajlovic_LEAP_Learning_Articulated_Occupancy_of_People_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mihajlovic_LEAP_Learning_Articulated_CVPR_2021_supplemental.pdf | null |
ANR: Articulated Neural Rendering for Virtual Avatars | Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, Christoph Lassner | Deferred Neural Rendering (DNR) uses a three-step pipeline to translate a mesh representation into an RGB image. The combination of a traditional rendering stack with neural networks hits a sweet spot in terms of computational complexity and realism of the resulting images. Using skinned meshes for animatable objects is a natural extension for the framework and would open it up to a plethora of applications. However, in this case the neural shading step must account for deformations that are possibly not captured in the mesh, as well as alignment accuracies and dynamics---which is not well-supported in the DNR pipeline. In this paper, we present an in-depth study of possibilities to develop the DNR framework towards handling these cases. We outline several steps that can be easily integrated into the DNR pipeline for addressing stability and deformation. We demonstrate their efficiency by building a virtual avatar pipeline, a highly challenging case with animation and clothing deformation, and show the superiority of the presented method not only with respect to the DNR pipeline but also with methods specifically for virtual avatar creation and animation. In two user studies, we observe a clear preference for our avatar model and outperform other methods on SSIM and LPIPS metrics. Perceptually, we observe better temporal stability, level of detail and plausibility. | https://openaccess.thecvf.com/content/CVPR2021/papers/Raj_ANR_Articulated_Neural_Rendering_for_Virtual_Avatars_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.12890 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_ANR_Articulated_Neural_Rendering_for_Virtual_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_ANR_Articulated_Neural_Rendering_for_Virtual_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Raj_ANR_Articulated_Neural_CVPR_2021_supplemental.pdf | null |
Flow-Based Kernel Prior With Application to Blind Super-Resolution | Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, Radu Timofte | Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, while KernelGAN employs the deep linear network and several regularization losses to constrain the kernel space. However, they fail to fully exploit the general SR kernel assumption that anisotropic Gaussian kernels are sufficient for image SR. To address this issue, this paper proposes a normalizing flow-based kernel prior (FKP) for kernel modeling. By learning an invertible mapping between the anisotropic Gaussian kernel distribution and a tractable latent distribution, FKP can be easily used to replace the kernel modeling modules of Double-DIP and KernelGAN. Specifically, FKP optimizes the kernel in the latent space rather than the network parameter space, which allows it to generate reasonable kernel initialization, traverse the learned kernel manifold and improve the optimization stability. Extensive experiments on synthetic and real-world images demonstrate that the proposed FKP can significantly improve the kernel estimation accuracy with less parameters, runtime and memory usage, leading to state-of-the-art blind SR results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_Flow-Based_Kernel_Prior_With_Application_to_Blind_Super-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15977 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Flow-Based_Kernel_Prior_With_Application_to_Blind_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Flow-Based_Kernel_Prior_With_Application_to_Blind_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liang_Flow-Based_Kernel_Prior_CVPR_2021_supplemental.pdf | null |
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services | Jinyu Tian, Jiantao Zhou, Jia Duan | Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to users. Our algorithm firstly selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting 8% parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Probabilistic_Selective_Encryption_of_Convolutional_Neural_Networks_for_Hierarchical_Services_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.12344 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Probabilistic_Selective_Encryption_of_Convolutional_Neural_Networks_for_Hierarchical_Services_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Probabilistic_Selective_Encryption_of_Convolutional_Neural_Networks_for_Hierarchical_Services_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Probabilistic_Selective_Encryption_CVPR_2021_supplemental.pdf | null |
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images | Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn | Humans perceive and construct the surrounding world as an arrangement of simple parametric models. In particular, man-made environments commonly consist of volumetric primitives such as cuboids or cylinders. Inferring these primitives is an important step to attain high-level, abstract scene descriptions. Previous approaches directly estimate shape parameters from a 2D or 3D input, and are only able to reproduce simple objects, yet unable to accurately parse more complex 3D scenes. In contrast, we propose a robust estimator for primitive fitting, which can meaningfully abstract real-world environments using cuboids. A RANSAC estimator guided by a neural network fits these primitives to 3D features, such as a depth map. We condition the network on previously detected parts of the scene, thus parsing it one-by-one. To obtain 3D features from a single RGB image, we additionally optimise a feature extraction CNN in an end-to-end manner. However, naively minimising point-to-primitive distances leads to large or spurious cuboids occluding parts of the scene behind. We thus propose an occlusion-aware distance metric correctly handling opaque scenes. The proposed algorithm does not require labour-intensive labels, such as cuboid annotations, for training. Results on the challenging NYU Depth v2 dataset demonstrate that the proposed algorithm successfully abstracts cluttered real-world 3D scene layouts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kluger_Cuboids_Revisited_Learning_Robust_3D_Shape_Fitting_to_Single_RGB_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kluger_Cuboids_Revisited_Learning_Robust_3D_Shape_Fitting_to_Single_RGB_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kluger_Cuboids_Revisited_Learning_Robust_3D_Shape_Fitting_to_Single_RGB_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kluger_Cuboids_Revisited_Learning_CVPR_2021_supplemental.pdf | null |
Dive Into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition | Jiahui She, Yibo Hu, Hailin Shi, Jun Wang, Qiu Shen, Tao Mei | Due to the subjective annotation and the inherent inter-class similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity. In this paper, we proposes a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives: the latent Distribution Mining and the pairwise Uncertainty Estimation. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space. The proposed method is independent to the backbone architectures, and brings no extra burden for inference. The experiments are conducted on the popular real-world benchmarks and the synthetic noisy datasets. Either way, the proposed DMUE stably achieves leading performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/She_Dive_Into_Ambiguity_CVPR_2021_supplemental.pdf | null |
Attention-Guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton | Xi Zhang, Xiaolin Wu | We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and near a saliency sketch within ROI that are critical to perceptual quality. Only the critical pixels are further sampled by compressive sensing (CS) to form a very compact refinement layer. Another novel CNN method is developed to jointly decode the two compression code layers for a much refined reconstruction, while strictly satisfying the transmitted CS constraints on perceptually critical pixels. Extensive experiments demonstrate that the proposed AGDL system advances the state of the art in perception-aware image compression. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Attention-Guided_Image_Compression_by_Deep_Reconstruction_of_Compressive_Sensed_Saliency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Attention-Guided_Image_Compression_by_Deep_Reconstruction_of_Compressive_Sensed_Saliency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Attention-Guided_Image_Compression_by_Deep_Reconstruction_of_Compressive_Sensed_Saliency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Attention-Guided_Image_Compression_CVPR_2021_supplemental.pdf | null |
Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering | Huafeng Liu, Jiaqi Wang, Liping Jing | In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac). It provides an efficient neural clustering architecture by grouping data points in a cluster-wise view rather than point-wise view. CHiGac simultaneously learns what makes a cluster, how to group data points into clusters, and how to adaptively control the number of clusters. The dedicated cluster generative process is able to sufficiently exploit pair-wise or higher-order interactions between data points in both inter- and intra-cluster, which is useful to sufficiently mine the hidden structure among data. To efficiently minimize the generalized lower bound of CHiGac, we design an Ergodic Amortized Inference (EAI) strategy by considering the average behavior over sequence on an inner variational parameter trajectory, which is theoretically proven to reduce the amortization gap. A series of experiments have been conducted on both synthetic and real-world data. The experimental results demonstrated that CHiGac can efficiently and accurately cluster datasets in terms of both internal and external evaluation metrics (DBI and ACC). | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Cluster-Wise_Hierarchical_Generative_Model_for_Deep_Amortized_Clustering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cluster-Wise_Hierarchical_Generative_Model_for_Deep_Amortized_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cluster-Wise_Hierarchical_Generative_Model_for_Deep_Amortized_Clustering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Cluster-Wise_Hierarchical_Generative_CVPR_2021_supplemental.pdf | null |
Mirror3D: Depth Refinement for Mirror Surfaces | Jiaqi Tan, Weijie Lin, Angel X. Chang, Manolis Savva | Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterpot3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We then develop Mirror3DNet: a module that refines raw sensor depth or estimated depth to correct errors on mirror surfaces. Our key idea is to estimate the 3D mirror plane based on RGB input and surrounding depth context, and use this estimate to directly regress mirror surface depth. Our experiments show that Mirror3DNet significantly mitigates errors from a variety of input depth data, including raw sensor depth and depth estimation or completion methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Mirror3D_Depth_Refinement_for_Mirror_Surfaces_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.06629 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Mirror3D_Depth_Refinement_for_Mirror_Surfaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Mirror3D_Depth_Refinement_for_Mirror_Surfaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Mirror3D_Depth_Refinement_CVPR_2021_supplemental.pdf | null |
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning | Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, Han Hu | Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions. In this paper, we introduce pixel-level pretext tasks for learning dense feature representations. The first task directly applies contrastive learning at the pixel level. We additionally propose a pixel-to-propagation consistency task that produces better results, even surpassing the state-of-the-art approaches by a large margin. Specifically, it achieves 60.2 AP, 41.4 / 40.5 mAP and 77.2 mIoU when transferred to Pascal VOC object detection (C4), COCO object detection (FPN / C4) and Cityscapes semantic segmentation using a ResNet-50 backbone network, which are 2.6 AP, 0.8 / 1.0 mAP and 1.0 mIoU better than the previous best methods built on instance-level contrastive learning. Moreover, the pixel-level pretext tasks are found to be effective for pre-training not only regular backbone networks but also head networks used for dense downstream tasks, and are complementary to instance-level contrastive methods. These results demonstrate the strong potential of defining pretext tasks at the pixel level, and suggest a new path forward in unsupervised visual representation learning. Code is available at https://github.com/zdaxie/PixPro. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Propagate_Yourself_Exploring_Pixel-Level_Consistency_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10043 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Propagate_Yourself_Exploring_Pixel-Level_Consistency_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Propagate_Yourself_Exploring_Pixel-Level_Consistency_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Reciprocal Transformations for Unsupervised Video Object Segmentation | Sucheng Ren, Wenxi Liu, Yongtuo Liu, Haoxin Chen, Guoqiang Han, Shengfeng He | Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and rely on optical flow to capture the motion cues in videos, but the flow information alone is insufficient to distinguish the primary objects from the background objects that move together. This is because, when the noisy motion features are combined with the appearance features, the localization of the primary objects is misguided. To address this problem, we propose a novel reciprocal transformation network to discover primary objects by correlating three key factors: the intra-frame contrast, the motion cues, and temporal coherence of recurring objects. Each corresponds to a representative type of primary object, and our reciprocal mechanism enables an organic coordination of them to effectively remove ambiguous distractions from videos. Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers. Experiments on the public benchmarks demonstrate that our model significantly outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Reciprocal_Transformations_for_Unsupervised_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ren_Reciprocal_Transformations_for_CVPR_2021_supplemental.zip | null |
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark | Longyin Wen, Dawei Du, Pengfei Zhu, Qinghua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu | To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured large-scale dataset, named as DroneCrowd, formed by 112 video clips with 33,600 HD frames in various scenarios. Notably, we annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. Meanwhile, we design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds. STNNet is formed by the feature extraction module, followed by the density map estimation heads, and localization and association subnets. To exploit the context information of neighboring objects, we design the neighboring context loss to guide the association subnet training, which enforces consistent relative position of nearby objects in temporal domain. Extensive experiments on our DroneCrowd dataset demonstrate that STNNet performs favorably against the state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Detection_Tracking_and_Counting_Meets_Drones_in_Crowds_A_Benchmark_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Detection_Tracking_and_Counting_Meets_Drones_in_Crowds_A_Benchmark_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Detection_Tracking_and_Counting_Meets_Drones_in_Crowds_A_Benchmark_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Complete 3D Morphable Face Models From Images and Videos | Mallikarjun B R, Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt | Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity and expression geometry, and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expressions. We present the first approach to learn complete 3D models of face identity and expression geometry, and reflectance, just from images and videos. The virtually endless collection of such data, in combination with our self-supervised learning-based approach allows for learning face models that generalize beyond the span of existing approaches. Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis. Our method also allows for in-the-wild monocular reconstruction at test time. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches. We show that the learned model can also be personalized to a video, for a better capture of the geometry and albedo. | https://openaccess.thecvf.com/content/CVPR2021/papers/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.01679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/R_Learning_Complete_3D_CVPR_2021_supplemental.pdf | null |
Bottom-Up Shift and Reasoning for Referring Image Segmentation | Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, Yizhou Yu | Referring image segmentation aims to segment the referent that is the corresponding object or stuff referred by a natural language expression in an image. Its main challenge lies in how to effectively and efficiently differentiate between the referent and other objects of the same category as the referent. In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules. Specifically, BUS progressively locates the referent along hierarchical reasoning steps implied by the expression. At each step, it locates the corresponding visual region by disambiguating between similar regions, where the disambiguation bases on the relationships between regions. By the explainable visual reasoning, BUS explicitly aligns linguistic components with visual regions so that it can identify all the mentioned entities in the expression. BIAR fuses multi-level features via a two-way attentive message passing, which captures the visual details relevant to the referent to refine segmentation results. Experimental results demonstrate that the proposed method consisting of BUS and BIAR modules, can not only consistently surpass all existing state-of-the-art algorithms across common benchmark datasets but also visualize interpretable reasoning steps for stepwise segmentation. Code is available at https://github.com/incredibleXM/BUSNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Bottom-Up_Shift_and_Reasoning_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Bottom-Up_Shift_and_Reasoning_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Bottom-Up_Shift_and_Reasoning_for_Referring_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion | Vitor Guizilini, Rares Ambrus, Wolfram Burgard, Adrien Gaidon | Estimating scene geometry from cost-effective sensors is key for robots. In this paper, we study the problem of predicting dense depth from a single RGB image (monodepth) with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Auxiliary Networks (SAN), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion, depending on whether only RGB images or also sparse point clouds are available at inference time. First, we decouple the image and depth map encoding stages using sparse convolutions to process only the valid depth map pixels. Second, we inject this information, when available, into the skip connections of the depth prediction network, augmenting its features. Through extensive experimental analysis on one indoor (NYUv2) and two outdoor (KITTI and DDAD) benchmarks, we demonstrate that our proposed SAN architecture is able to simultaneously learn both tasks, while achieving a new state of the art in depth prediction by a significant margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guizilini_Sparse_Auxiliary_Networks_for_Unified_Monocular_Depth_Prediction_and_Completion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guizilini_Sparse_Auxiliary_Networks_for_Unified_Monocular_Depth_Prediction_and_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guizilini_Sparse_Auxiliary_Networks_for_Unified_Monocular_Depth_Prediction_and_Completion_CVPR_2021_paper.html | CVPR 2021 | null | null |
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes With Biharmonic Coordinates | Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su | We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points' translations to the entire mesh. To avoid learning zero deformation as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations' plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_DeepMetaHandles_Learning_Deformation_Meta-Handles_of_3D_Meshes_With_Biharmonic_Coordinates_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.09105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeepMetaHandles_Learning_Deformation_Meta-Handles_of_3D_Meshes_With_Biharmonic_Coordinates_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeepMetaHandles_Learning_Deformation_Meta-Handles_of_3D_Meshes_With_Biharmonic_Coordinates_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_DeepMetaHandles_Learning_Deformation_CVPR_2021_supplemental.pdf | null |
Panoptic Segmentation Forecasting | Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander G. Schwing | Our goal is to forecast the near future given a set of recent observations. We think this ability to forecast, i.e., to anticipate, is integral for the success of autonomous agents which need not only passively analyze an observation but also must react to it in real-time. Importantly, accurate forecasting hinges upon the chosen scene decomposition. We think that superior forecasting can be achieved by decomposing a dynamic scene into individual 'things' and background 'stuff'. Background 'stuff' largely moves because of camera motion, while foreground 'things' move because of both camera and individual object motion. Following this decomposition, we introduce panoptic segmentation forecasting. Panoptic segmentation forecasting opens up a middle-ground between existing extremes, which either forecast instance trajectories or predict the appearance of future image frames. To address this task we develop a two-component model: one component learns the dynamics of the background stuff by anticipating odometry, the other one anticipates the dynamics of detected things. We establish a leaderboard for this novel task, and validate a state-of-the-art model that outperforms available baselines. | https://openaccess.thecvf.com/content/CVPR2021/papers/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03962 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Graber_Panoptic_Segmentation_Forecasting_CVPR_2021_supplemental.zip | null |
SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection | Weichen Zhang, Wen Li, Dong Xu | Geometric characteristic plays an important role in the representation of an object in 3D point clouds. For example, large objects often contain more points, while small ones contain fewer points. The point clouds of objects near the capture device are denser, while those of distant objects are sparser. These issues bring new challenges to 3D object detection, especially under the domain adaptation scenarios. In this work, we propose a new cross-dataset 3D object detection method named Scale-aware and Range-aware Domain Adaptation Network (SRDAN). We take advantage of the geometric characteristics of 3D data (i.e., size and distance), and propose the scale-aware domain alignment and the range-aware domain alignment strategies to guide the distribution alignment between two domains. For scale-aware domain alignment, we design a 3D voxel-based feature pyramid network to extract multi-scale semantic voxel features, and align the features and instances with similar scales between two domains. For range-aware domain alignment, we introduce a range-guided domain alignment module to align the features of objects according to their distance to the capture device. Extensive experiments under three different scenarios demonstrate the effectiveness of our SRDAN approach, and comprehensive ablation study also validates the importance of geometric characteristics for cross-dataset 3D object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_SRDAN_Scale-Aware_and_Range-Aware_Domain_Adaptation_Network_for_Cross-Dataset_3D_CVPR_2021_paper.html | CVPR 2021 | null | null |
Pedestrian and Ego-Vehicle Trajectory Prediction From Monocular Camera | Lukas Neumann, Andrea Vedaldi | Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e.g. breaking) to take place. In this paper, we propose a new method to predict future position of pedestrians, with respect to a predicted future position of the ego-vehicle, thus giving a assistive/autonomous driving system sufficient time to respond. The method explicitly disentangles actual movement of pedestrians in real world from the ego-motion of the vehicle, using a future pose prediction network trained in self-supervised fashion, which allows the method to observe and predict the intrinsic pedestrian motion in a normalised view, that captures the same real-world location across multiple frames. The method is evaluated on two public datasets, where it achieves state-of-the-art results in pedestrian trajectory prediction from an on-board camera. | https://openaccess.thecvf.com/content/CVPR2021/papers/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.html | CVPR 2021 | null | null |
Globally Optimal Relative Pose Estimation With Gravity Prior | Yaqing Ding, Daniel Barath, Jian Yang, Hui Kong, Zuzana Kukelova | Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions and ground truth 3D reconstructions. The source code and dataset are available at https://github.com/yaqding/opt_pose_gravity | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_Globally_Optimal_Relative_Pose_Estimation_With_Gravity_Prior_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00458 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Globally_Optimal_Relative_Pose_Estimation_With_Gravity_Prior_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Globally_Optimal_Relative_Pose_Estimation_With_Gravity_Prior_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ding_Globally_Optimal_Relative_CVPR_2021_supplemental.pdf | null |
Mutual CRF-GNN for Few-Shot Learning | Shixiang Tang, Dapeng Chen, Lei Bai, Kaijian Liu, Yixiao Ge, Wanli Ouyang | Graph-neural-networks (GNN) is a rising trend for few-shot learning. A critical component in GNN is the affinity. Typically, affinity in GNN is mainly computed in the feature space, e.g., pairwise features, and does not take fully advantage of semantic labels associated to these features. In this paper, we propose a novel Mutual CRF-GNN (MCGN). In this MCGN, the labels and features of support data are used by the CRF for inferring GNN affinities in a principled and probabilistic way. Specifically, we construct a Conditional Random Field (CRF) conditioned on labels and features of support data to infer a affinity in the label space. Such affinity is fed to the GNN as the node-wise affinity. GNN and CRF mutually contributes to each other in MCGN. For GNN, CRF provides valuable affinity information. For CRF, GNN provides better features for inferring affinity. Experimental results show that our approach outperforms state-of-the-arts on datasets miniImageNet, tieredImageNet, and CIFAR-FS on both 5-way 1-shot and 5-way 5-shot settings. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Mutual_CRF-GNN_for_Few-Shot_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Mutual_CRF-GNN_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Mutual_CRF-GNN_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Mutual_CRF-GNN_for_CVPR_2021_supplemental.pdf | null |
Weakly Supervised Action Selection Learning in Video | Junwei Ma, Satya Krishna Gorti, Maksims Volkovs, Guangwei Yu | Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction. Frame-level activations are then used for localization. However, the absence of frame-level annotations cause the classifier to impart class bias on every frame. To address this, we propose the Action Selection Learning (ASL) approach to capture the general concept of action, a property we refer to as "actionness". Under ASL, the model is trained with a novel class-agnostic task to predict which frames will be selected by the classifier. Empirically, we show that ASL outperforms leading baselines on two popular benchmarks THUMOS-14 and ActivityNet-1.2, with 10.3% and 5.7% relative improvement respectively. We further analyze the properties of ASL and demonstrate the importance of actionness. Full code for this work is available here https://github.com/layer6ai-labs/ASL | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02439 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.html | CVPR 2021 | null | null |
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