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3D Shape Generation With Grid-Based Implicit Functions | Moritz Ibing, Isaak Lim, Leif Kobbelt | Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it is difficult to add spatial supervision into the generation process, as the AE only gives us a global representation. To remedy these issues, we propose to train the GAN on grids (i.e. each cell covers a part of a shape). In this representation each cell is equipped with a latent vector provided by an AE. This localized representation enables more expressiveness (since the cell-based latent vectors can be combined in novel ways) as well as spatial control of the generation process (e.g. via bounding boxes). Our method outperforms the current state of the art on all established evaluation measures, proposed for quantitatively evaluating the generative capabilities of GANs. We show limitations of these measures and propose the adaptation of a robust criterion from statistical analysis as an alternative. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ibing_3D_Shape_Generation_CVPR_2021_supplemental.pdf | null |
Tangent Space Backpropagation for 3D Transformation Groups | Zachary Teed, Jia Deng | We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in 3D vision and robotics, but they do not form vector spaces and instead lie on smooth manifolds. The standard backpropagation approach, which embeds 3D transformations in Euclidean spaces, suffers from numerical difficulties. We introduce a new library, which exploits the group structure of 3D transformations and performs backpropagation in the tangent spaces of manifolds. We show that our approach is numerically more stable, easier to implement, and beneficial to a diverse set of tasks. Our plug-and-play PyTorch library is available at https://github.com/princeton-vl/lietorch. | https://openaccess.thecvf.com/content/CVPR2021/papers/Teed_Tangent_Space_Backpropagation_for_3D_Transformation_Groups_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12032 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Teed_Tangent_Space_Backpropagation_for_3D_Transformation_Groups_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Teed_Tangent_Space_Backpropagation_for_3D_Transformation_Groups_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Teed_Tangent_Space_Backpropagation_CVPR_2021_supplemental.pdf | null |
FAIEr: Fidelity and Adequacy Ensured Image Caption Evaluation | Sijin Wang, Ziwei Yao, Ruiping Wang, Zhongqin Wu, Xilin Chen | Image caption evaluation is a crucial task, which involves the semantic perception and matching of image and text. Good evaluation metrics aim to be fair, comprehensive, and consistent with human judge intentions. When humans evaluate a caption, they usually consider multiple aspects, such as whether it is related to the target image without distortion, how much image gist it conveys, as well as how fluent and beautiful the language and wording is. The above three different evaluation orientations can be summarized as fidelity, adequacy, and fluency. The former two rely on the image content, while fluency is purely related to linguistics and more subjective. Inspired by human judges, we propose a learning-based metric named FAIEr to ensure evaluating the fidelity and adequacy of the captions. Since image captioning involves two different modalities, we employ the scene graph as a bridge between them to represent both images and captions. FAIEr mainly regards the visual scene graph as the criterion to measure the fidelity. Then for evaluating the adequacy of the candidate caption, it highlights the image gist on the visual scene graph under the guidance of the reference captions. Comprehensive experimental results show that FAIEr has high consistency with human judgment as well as high stability, low reference dependency, and the capability of reference-free evaluation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_FAIEr_Fidelity_and_Adequacy_Ensured_Image_Caption_Evaluation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_FAIEr_Fidelity_and_Adequacy_Ensured_Image_Caption_Evaluation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_FAIEr_Fidelity_and_Adequacy_Ensured_Image_Caption_Evaluation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_FAIEr_Fidelity_and_CVPR_2021_supplemental.pdf | null |
HLA-Face: Joint High-Low Adaptation for Low Light Face Detection | Wenjing Wang, Wenhan Yang, Jiaying Liu | Face detection in low light scenarios is challenging but vital to many practical applications, e.g., surveillance video, autonomous driving at night. Most existing face detectors heavily rely on extensive annotations, while collecting data is time-consuming and laborious. To reduce the burden of building new datasets for low light conditions, we make full use of existing normal light data and explore how to adapt face detectors from normal light to low light. The challenge of this task is that the gap between normal and low light is too huge and complex for both pixel-level and object-level. Therefore, most existing low-light enhancement and adaptation methods do not achieve desirable performance. To address the issue, we propose a joint High-Low Adaptation (HLA) framework. Through a bidirectional low-level adaptation and multi-task high-level adaptation scheme, our HLA-Face outperforms state-of-the-art methods even without using dark face labels for training. Our project is publicly available at: https://daooshee.github.io/HLA-Face-Website/ | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_HLA-Face_Joint_High-Low_CVPR_2021_supplemental.pdf | null |
Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions | Navaneeth Bodla, Gaurav Shrivastava, Rama Chellappa, Abhinav Shrivastava | Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation. An action sequence for a human-object interaction task is typically very complicated, involving the evolution of pose, person's appearance, object locations, and object appearances over time. To this end, we propose a Hierarchical Video Prediction model using Relational Layouts. In the first stage, we learn to predict a sequence of layouts. A layout is a high-level representation of the video containing both pose and objects' information for every frame. The layout sequence is learned by modeling the relationships between the pose and objects using relational reasoning and recurrent neural networks. The layout sequence acts as a strong structure prior to the second stage that learns to map the layouts into pixel space. Experimental evaluation of our method on two datasets, UMD-HOI and Bimanual, shows significant improvements in standard video evaluation metrics such as LPIPS, PSNR, and SSIM. We also perform a detailed qualitative analysis of our model to demonstrate various generalizations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bodla_Hierarchical_Video_Prediction_Using_Relational_Layouts_for_Human-Object_Interactions_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bodla_Hierarchical_Video_Prediction_Using_Relational_Layouts_for_Human-Object_Interactions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bodla_Hierarchical_Video_Prediction_Using_Relational_Layouts_for_Human-Object_Interactions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bodla_Hierarchical_Video_Prediction_CVPR_2021_supplemental.pdf | null |
From Rain Generation to Rain Removal | Hong Wang, Zongsheng Yue, Qi Xie, Qian Zhao, Yefeng Zheng, Deyu Meng | For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most of current state-of-the-art focus on constructing powerful deep models to obtain better deraining results. In this paper, to further improve the deraining performance, we novelly attempt to handle the SIRR task from the perspective of training datasets by exploring a more efficient way to synthesize rainy images. Specifically, we build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator with the input as some latent variables representing the physical structural rain factors, e.g., direction, scale, and thickness. To solve this model, we employ the variational inference framework to approximate the expected statistical distribution of rainy image in a data-driven manner. With the learned generator, we can automatically and sufficiently generate diverse and non-repetitive training pairs so as to efficiently enrich and augment the existing benchmark datasets. User study qualitatively and quantitatively evaluates the realism of generated rainy images. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution that not only helps significantly improve the deraining performance of current deep single image derainers, but also largely loosens the requirement of large training sample pre-collection for the SIRR task. Code is available in https://github.com/hongwang01/VRGNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.03580 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_From_Rain_Generation_CVPR_2021_supplemental.zip | null |
Few-Shot Classification With Feature Map Reconstruction Networks | Davis Wertheimer, Luming Tang, Bharath Hariharan | In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wertheimer_Few-Shot_Classification_With_Feature_Map_Reconstruction_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01506 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wertheimer_Few-Shot_Classification_With_Feature_Map_Reconstruction_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wertheimer_Few-Shot_Classification_With_Feature_Map_Reconstruction_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wertheimer_Few-Shot_Classification_With_CVPR_2021_supplemental.pdf | null |
Object Classification From Randomized EEG Trials | Hamad Ahmed, Ronnie B. Wilbur, Hari M. Bharadwaj, Jeffrey Mark Siskind | New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we attempt object classification from EEG using an array of methods that are representative of the state-of-the-art, with a far larger (20x) dataset of randomized EEG trials, 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data-collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection with only marginally above-chance performance suggests that the prevailing literature substantially exaggerates the feasibility of object classification from EEG. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmed_Object_Classification_From_Randomized_EEG_Trials_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.06046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmed_Object_Classification_From_Randomized_EEG_Trials_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmed_Object_Classification_From_Randomized_EEG_Trials_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ahmed_Object_Classification_From_CVPR_2021_supplemental.zip | null |
Learning Monocular 3D Reconstruction of Articulated Categories From Motion | Filippos Kokkinos, Iasonas Kokkinos | Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem. In this work we use video self-supervision, forcing the consistency of consecutive 3D reconstructions by a motion-based cycle loss. This largely improves both optimization-based and learning-based 3D mesh reconstruction. We further introduce an interpretable model of 3D template deformations that controls a 3D surface through the displacement of a small number of local, learnable handles. We formulate this operation as a structured layer relying on mesh-laplacian regularization and show that it can be trained in an end-to-end manner. We finally introduce a per-sample numerical optimisation approach that jointly optimises over mesh displacements and cameras within a video, boosting accuracy both for training and also as test time post-processing. While relying exclusively on a small set of videos collected per category for supervision, we obtain state-of-the-art reconstructions with diverse shapes, viewpoints and textures for multiple articulated object categories. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kokkinos_Learning_Monocular_3D_Reconstruction_of_Articulated_Categories_From_Motion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16352 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kokkinos_Learning_Monocular_3D_Reconstruction_of_Articulated_Categories_From_Motion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kokkinos_Learning_Monocular_3D_Reconstruction_of_Articulated_Categories_From_Motion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kokkinos_Learning_Monocular_3D_CVPR_2021_supplemental.pdf | null |
De-Rendering the World's Revolutionary Artefacts | Shangzhe Wu, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa | Recent works have shown exciting results in unsupervised image de-rendering--learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR, that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world's revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. More results and code at: https://sorderender.github.io/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_De-Rendering_the_Worlds_Revolutionary_Artefacts_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_De-Rendering_the_Worlds_Revolutionary_Artefacts_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_De-Rendering_the_Worlds_Revolutionary_Artefacts_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_De-Rendering_the_Worlds_CVPR_2021_supplemental.pdf | null |
Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow | Shangrong Yang, Chunyu Lin, Kang Liao, Chunjie Zhang, Yao Zhao | Distortion rectification is often required for fisheye images. The generation-based method is one mainstream solution due to its label-free property, but its naive skip-connection and overburdened decoder will cause blur and incomplete correction. First, the skip-connection directly transfers the image features, which may introduce distortion and cause incomplete correction. Second, the decoder is overburdened during simultaneously reconstructing the content and structure of the image, resulting in vague performance. To solve these two problems, in this paper, we focus on the interpretable correction mechanism of the distortion rectification network and propose a feature-level correction scheme. We embed a correction layer in skip-connection and leverage the appearance flows in different layers to pre-correct the image features. Consequently, the decoder can easily reconstruct a plausible result with the remaining distortion-less information. In addition, we propose a parallel complementary structure. It effectively reduces the burden of the decoder by separating content reconstruction and structure correction. Subjective and objective experiment results on different datasets demonstrate the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Progressively_Complementary_Network_for_Fisheye_Image_Rectification_Using_Appearance_Flow_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Progressively_Complementary_Network_for_Fisheye_Image_Rectification_Using_Appearance_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Progressively_Complementary_Network_for_Fisheye_Image_Rectification_Using_Appearance_Flow_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Progressively_Complementary_Network_CVPR_2021_supplemental.pdf | null |
DECOR-GAN: 3D Shape Detailization by Conditional Refinement | Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri | We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exemplars by treating the problem as that of geometric detail transfer. Given a low-resolution coarse voxel shape, our network refines it, via voxel upsampling, into a higher-resolution shape enriched with geometric details. The output shape preserves the overall structure (or content) of the input, while its detail generation is conditioned on an input "style code" corresponding to a detailed exemplar. Our 3D detailization via conditional refinement is realized by a generative adversarial network, coined DECOR-GAN. The network utilizes a 3D CNN generator for upsampling coarse voxels and a 3D PatchGAN discriminator to enforce local patches of the generated model to be similar to those in the training detailed shapes. During testing, a style code is fed into the generator to condition the refinement. We demonstrate that our method can refine a coarse shape into a variety of detailed shapes with different styles. The generated results are evaluated in terms of content preservation, plausibility, and diversity. Comprehensive ablation studies are conducted to validate our network designs. Code is available at https://github.com/czq142857/DECOR-GAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_DECOR-GAN_3D_Shape_Detailization_by_Conditional_Refinement_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_DECOR-GAN_3D_Shape_Detailization_by_Conditional_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_DECOR-GAN_3D_Shape_Detailization_by_Conditional_Refinement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_DECOR-GAN_3D_Shape_CVPR_2021_supplemental.pdf | null |
Model-Aware Gesture-to-Gesture Translation | Hezhen Hu, Weilun Wang, Wengang Zhou, Weichao Zhao, Houqiang Li | Hand gesture-to-gesture translation is a significant and interesting problem, which serves as a key role in many applications, such as sign language production. This task involves fine-grained structure understanding of the mapping between the source and target gestures. Current works follow a data-driven paradigm based on sparse 2D joint representation. However, given the insufficient representation capability of 2D joints, this paradigm easily leads to blurry generation results with incorrect structure. In this paper, we propose a novel model-aware gesture-to-gesture translation framework, which introduces hand prior with hand meshes as the intermediate representation. To take full advantage of the structured hand model, we first build a dense topology map aligning the image plane with the encoded embedding of the visible hand mesh. Then, a transformation flow is calculated based on the correspondence of the source and target topology map. During the generation stage, we inject the topology information into generation streams by modulating the activations in a spatially-adaptive manner. Further, we incorporate the source local characteristic to enhance the translated gesture image according to the transformation flow. Extensive experiments on two benchmark datasets have demonstrated that our method achieves new state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Spatio-temporal Contrastive Domain Adaptation for Action Recognition | Xiaolin Song, Sicheng Zhao, Jingyu Yang, Huanjing Yue, Pengfei Xu, Runbo Hu, Hua Chai | Unsupervised domain adaptation (UDA) for human action recognition is a practical and challenging problem. Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with frame-level or clip-level features, which is not discriminative sufficiently for video-based UDA tasks. To address these problems, in this paper we propose to establish the cross-modal domain alignment via self-supervised contrastive framework, i.e., spatio-temporal contrastive domain adaptation (STCDA), to learn the joint clip-level and video-level representation alignment. Since the effective representation is modeled from unlabeled data by self-supervised learning (SSL), spatio-temporal contrastive learning (STCL) is proposed to explore the useful long-term feature representation for classification, using self-supervision setting trained from the contrastive clip/video pairs with positive or negative properties. Besides, we involve a novel domain metric scheme, i.e., video-based contrastive alignment (VCA), to optimize the category-aware video-level alignment and generalization between source and target. The proposed STCDA achieves stat-of-the-art results on several UDA benchmarks for action recognition. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Spatio-temporal_Contrastive_Domain_Adaptation_for_Action_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Spatio-temporal_Contrastive_Domain_Adaptation_for_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Spatio-temporal_Contrastive_Domain_Adaptation_for_Action_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Spatio-temporal_Contrastive_Domain_CVPR_2021_supplemental.pdf | null |
Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection | Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu | Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter of fact, AU semantic descriptions provide much more information than the binary AU labels alone, thus we propose to exploit the Semantic Embedding and Visual feature (SEV-Net) for AU detection. More specifically, AU semantic embeddings are obtained through both Intra-AU and Inter-AU attention modules, where the Intra-AU attention module captures the relation among words within each sentence that describes individual AU, and the Inter-AU attention module focuses on the relation among those sentences. The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network. The generated cross-modality attention maps are further used as weights for the aggregated feature. Our proposed method is unique in that the semantic features are exploited as the first of this kind. The approach has been evaluated on three public AU-coded facial expression databases, and has achieved a superior performance than the state-of-the-art peer methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Exploiting_Semantic_Embedding_and_Visual_Feature_for_Facial_Action_Unit_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Exploiting_Semantic_Embedding_and_Visual_Feature_for_Facial_Action_Unit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Exploiting_Semantic_Embedding_and_Visual_Feature_for_Facial_Action_Unit_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Exploiting_Semantic_Embedding_CVPR_2021_supplemental.pdf | null |
Categorical Depth Distribution Network for Monocular 3D Object Detection | Cody Reading, Ali Harakeh, Julia Chae, Steven L. Waslander | Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene cues due to the lack of direct range measurement. Many methods attempt to directly estimate depth to assist in 3D detection, but show limited performance as a result of depth inaccuracy. Our proposed solution, Categorical Depth Distribution Network (CaDDN), uses a predicted categorical depth distribution for each pixel to project rich contextual feature information to the appropriate depth interval in 3D space. We then use the computationally efficient bird's-eye-view projection and single-stage detector to produce the final output bounding boxes. We design CaDDN as a fully differentiable end-to-end approach for joint depth estimation and object detection. We validate our approach on the KITTI 3D object detection benchmark, where we rank 1st among published monocular methods. We also provide the first monocular 3D detection results on the newly released Waymo Open Dataset. We provide a code release for CaDDN which will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01100 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reading_Categorical_Depth_Distribution_CVPR_2021_supplemental.pdf | null |
Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading | Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He | Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal data could lead to an learning ambiguity and thus limits the performance of lip reading. In this paper, we propose a novel collaborative framework for lip reading, and two aspects of issues are considered: 1) the teacher should understand bi-modal knowledge to possibly bridge the inherent cross-modal gap; 2) the teacher should adjust teaching contents adaptively with the evolution of the student. To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher. The master produces logits from three modalities of features: audio modality, video modality, and their combination. To further provide an interactive strategy to fuse these knowledge organically, we regularize the master with the task-specific feedback from the student, in which the requirement of the student is implicitly embedded. Meanwhile we involve a couple of "tutor" networks into our system as guidance for emphasizing the fruitful knowledge flexibly. In addition, we incorporate a curriculum learning design to ensure a better convergence. Extensive experiments demonstrate that the proposed network outperforms the state-of-the-art methods on several benchmarks, including in both word-level and sentence-level scenarios. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Learning_From_the_Master_Distilling_Cross-Modal_Advanced_Knowledge_for_Lip_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Learning_From_the_Master_Distilling_Cross-Modal_Advanced_Knowledge_for_Lip_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Learning_From_the_Master_Distilling_Cross-Modal_Advanced_Knowledge_for_Lip_CVPR_2021_paper.html | CVPR 2021 | null | null |
Spatially-Varying Outdoor Lighting Estimation From Intrinsics | Yongjie Zhu, Yinda Zhang, Si Li, Boxin Shi | We present SOLID-Net, a neural network for spatially-varying outdoor lighting estimation from a single outdoor image for any 2D pixel location. Previous work has used a unified sky environment map to represent outdoor lighting. Instead, we generate spatially-varying local lighting environment maps by combining global sky environment map with warped image information according to geometric information estimated from intrinsics. As no outdoor dataset with image and local lighting ground truth is readily available, we introduce SOLID-Img dataset with physically-based rendered images and their corresponding intrinsic and lighting information. We train a deep neural network to regress intrinsic cues with physically-based constrains and use them to conduct global and local lightings estimation. Experiments on both synthetic and real datasets show that SOLID-Net significantly outperforms previous methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Spatially-Varying_Outdoor_Lighting_Estimation_From_Intrinsics_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Spatially-Varying_Outdoor_Lighting_Estimation_From_Intrinsics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Spatially-Varying_Outdoor_Lighting_Estimation_From_Intrinsics_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Spatially-Varying_Outdoor_Lighting_CVPR_2021_supplemental.pdf | null |
VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization | Seunghwan Choi, Sunghyun Park, Minsoo Lee, Jaegul Choo | The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthesized images is still limited to low (e.g., 256x192), which acts as the critical limitation against satisfying online consumers. We argue that the limitation stems from several challenges: as the resolution increases, the artifacts in the misaligned areas between the warped clothes and the desired clothing regions become noticeable in the final results; the architectures used in existing methods have low performance in generating high-quality body parts and maintaining the texture sharpness of the clothes. To address the challenges, we propose a novel virtual try-on method called VITON-HD that successfully synthesizes 1024x768 virtual try-on images. Specifically, we first prepare the segmentation map to guide our virtual try-on synthesis, and then roughly fit the target clothing item to a given person's body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS generator to handle the misaligned areas and preserve the details of 1024x768 inputs. Through rigorous comparison with existing methods, we demonstrate that VITON-HD highly surpasses the baselines in terms of synthesized image quality both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_VITON-HD_High-Resolution_Virtual_Try-On_via_Misalignment-Aware_Normalization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_VITON-HD_High-Resolution_Virtual_Try-On_via_Misalignment-Aware_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_VITON-HD_High-Resolution_Virtual_Try-On_via_Misalignment-Aware_Normalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_VITON-HD_High-Resolution_Virtual_CVPR_2021_supplemental.zip | null |
Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning | Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, Xiuyi Jia | During the last couple of years, convolutional neural networks (CNNs) have achieved significant success in the single image dehazing task. Unfortunately, most existing deep dehazing models have high computational complexity, which hinders their application to high-resolution images, especially for UHD (ultra-high-definition) or 4K resolution images. To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs. The first CNN extracts haze-relevant features at a reduced resolution of the hazy input and then fits locally-affine models in the bilateral space. Another CNN is used to learn multiple full-resolution guidance maps corresponding to the learned bilateral model. As a result, the feature maps with high-frequency can be reconstructed by multi-guided bilateral upsampling. Finally, the third CNN fuses the high-quality feature maps into a dehazed image. In addition, we create a large-scale 4K image dehazing dataset to support the training and testing of compared models. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing approaches on various benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
RankDetNet: Delving Into Ranking Constraints for Object Detection | Ji Liu, Dong Li, Rongzhang Zheng, Lu Tian, Yi Shan | Modern object detection approaches cast detecting objects as optimizing two subtasks of classification and localization simultaneously. Existing methods often learn the classification task by optimizing each proposal separately and neglect the relationship among different proposals. Such detection paradigm also encounters the mismatch between classification and localization due to the inherent discrepancy of their optimization targets. In this work, we propose a ranking-based optimization algorithm for harmoniously learning to rank and localize proposals in lieu of the classification task. To this end, we comprehensively investigate three types of ranking constraints, i.e., global ranking, class-specific ranking and IoU-guided ranking losses. The global ranking loss encourages foreground samples to rank higher than background. The class-specific ranking loss ensures that positive samples rank higher than negative ones for each specific class. The IoU-guided ranking loss aims to align each pair of confidence scores with the associated pair of IoU overlap between two positive samples of a specific class. Our ranking constraints can sufficiently explore the relationships between samples from three different perspectives. They are easy-to-implement, compatible with mainstream detection frameworks and computation-free for inference. Experiments demonstrate that our RankDetNet consistently surpasses prior anchor-based and anchor-free baselines, e.g., improving RetinaNet baseline by 2.5% AP on the COCO test-dev set without bells and whistles. We also apply the proposed ranking constraints for 3D object detection and achieve improved performance, which further validates the superiority and generality of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_RankDetNet_Delving_Into_Ranking_Constraints_for_Object_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_RankDetNet_Delving_Into_Ranking_Constraints_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_RankDetNet_Delving_Into_Ranking_Constraints_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_RankDetNet_Delving_Into_CVPR_2021_supplemental.pdf | null |
Back to the Feature: Learning Robust Camera Localization From Pixels To Pose | Paul-Edouard Sarlin, Ajaykumar Unagar, Mans Larsson, Hugo Germain, Carl Toft, Viktor Larsson, Marc Pollefeys, Vincent Lepetit, Lars Hammarstrand, Fredrik Kahl, Torsten Sattler | Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms. We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. Our approach is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching by jointly refining keypoints and poses with little overhead. The code will be publicly available at github.com/cvg/pixloc. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.09213 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sarlin_Back_to_the_CVPR_2021_supplemental.pdf | null |
Learning Parallel Dense Correspondence From Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction | Jiapeng Tang, Dan Xu, Kui Jia, Lei Zhang | This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. Despite the recent success achieved by extending deep implicit representations into 4D space, it is still a great challenge in two respects, i.e. how to design a flexible framework for learning robust spatio-temporal shape representations from 4D point clouds, and develop an efficient mechanism for capturing shape dynamics. In this work, we present a novel pipeline to learn a temporal evolution of the 3D human shape through spatially continuous transformation functions among cross-frame occupancy fields. The key idea is to parallelly establish the dense correspondence between predicted occupancy fields at different time steps via explicitly learning continuous displacement vector fields from robust spatio-temporal shape representations. Extensive comparisons against previous state-of-the-arts show the superior accuracy of our approach for 4D human reconstruction in the problems of 4D shape auto-encoding and completion, and a much faster network inference with about 8 times speedup demonstrates the significant efficiency of our approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Learning_Parallel_Dense_Correspondence_From_Spatio-Temporal_Descriptors_for_Efficient_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16341 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Learning_Parallel_Dense_Correspondence_From_Spatio-Temporal_Descriptors_for_Efficient_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Learning_Parallel_Dense_Correspondence_From_Spatio-Temporal_Descriptors_for_Efficient_and_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving | Aditya Prakash, Kashyap Chitta, Andreas Geiger | How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key, e.g. a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light. Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models. In this work, we demonstrate that imitation learning policies based on existing sensor fusion methods under-perform in the presence of a high density of dynamic agents and complex scenarios, which require global contextual reasoning, such as handling traffic oncoming from multiple directions at uncontrolled intersections. Therefore, we propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. We experimentally validate the efficacy of our approach in urban settings involving complex scenarios using the CARLA urban driving simulator. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Prakash_Multi-Modal_Fusion_Transformer_for_End-to-End_Autonomous_Driving_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09224 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Prakash_Multi-Modal_Fusion_Transformer_for_End-to-End_Autonomous_Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Prakash_Multi-Modal_Fusion_Transformer_for_End-to-End_Autonomous_Driving_CVPR_2021_paper.html | CVPR 2021 | null | null |
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search | Bin Yan, Houwen Peng, Kan Wu, Dong Wang, Jianlong Fu, Huchuan Lu | Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers. Comprehensive experiments show that our LightTrack is effective. It can find trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ and Ocean, while using much fewer model Flops and parameters. Moreover, when deployed on resource-constrained mobile chipsets, the discovered trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack runs 12x faster than Ocean, while using 13x fewer parameters and 38x fewer Flops. Such improvements might narrow the gap between academic models and industrial deployments in object tracking task. LightTrack is released at here. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_LightTrack_Finding_Lightweight_Neural_Networks_for_Object_Tracking_via_One-Shot_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14545 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_LightTrack_Finding_Lightweight_Neural_Networks_for_Object_Tracking_via_One-Shot_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_LightTrack_Finding_Lightweight_Neural_Networks_for_Object_Tracking_via_One-Shot_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_LightTrack_Finding_Lightweight_CVPR_2021_supplemental.zip | null |
Unsupervised Disentanglement of Linear-Encoded Facial Semantics | Yutong Zheng, Yu-Kai Huang, Ran Tao, Zhiqiang Shen, Marios Savvides | We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Unsupervised_Disentanglement_of_Linear-Encoded_Facial_Semantics_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Unsupervised_Disentanglement_of_Linear-Encoded_Facial_Semantics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Unsupervised_Disentanglement_of_Linear-Encoded_Facial_Semantics_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Unsupervised_Disentanglement_of_CVPR_2021_supplemental.pdf | null |
Learning Position and Target Consistency for Memory-Based Video Object Segmentation | Li Hu, Peng Zhang, Bang Zhang, Pan Pan, Yinghui Xu, Rong Jin | This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to learn position and target consistency framework for memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04329 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation | Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, Yong Wang, Fang Wen | Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.10979 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Prototypical_Pseudo_Label_CVPR_2021_supplemental.pdf | null |
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments | Zhihao Xia, Michael Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti | We introduce a neural network-based method to denoise pairs of images taken in quick succession in low-light environments, with and without a flash. Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image, while recovering surface texture and detail revealed by the flash. Our network outputs a gain map and a field of kernels, the latter obtained by linearly mixing elements of a per-image low-rank kernel basis. We first apply the kernel field to the no-flash image, and then multiply the result with the gain map to create the final output. We show our network effectively learns to produce high-quality images by combining a smoothed out estimate of the scene's ambient appearance from the no-flash image, with high-frequency albedo details extracted from the flash input. Our experiments show significant improvements over alternative captures without a flash, and baseline denoisers that use flash no-flash pairs. In particular, our method produces images that are both noise-free and contain accurate ambient colors without the sharp shadows or strong specular highlights visible in the flash image. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xia_Deep_Denoising_of_CVPR_2021_supplemental.pdf | null |
Transformer Interpretability Beyond Attention Visualization | Hila Chefer, Shir Gur, Lior Wolf | Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chefer_Transformer_Interpretability_Beyond_CVPR_2021_supplemental.pdf | null |
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach | Giang Truong, Huu Le, David Suter, Erchuan Zhang, Syed Zulqarnain Gilani | Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for highly contaminated datasets is, however, still challenging due to its underlying computational complexity. Recent attention has been focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems. | https://openaccess.thecvf.com/content/CVPR2021/papers/Truong_Unsupervised_Learning_for_Robust_Fitting_A_Reinforcement_Learning_Approach_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Unsupervised_Learning_for_Robust_Fitting_A_Reinforcement_Learning_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Unsupervised_Learning_for_Robust_Fitting_A_Reinforcement_Learning_Approach_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Truong_Unsupervised_Learning_for_CVPR_2021_supplemental.pdf | null |
Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training | Yunxuan Wei, Shuhang Gu, Yawei Li, Radu Timofte, Longcun Jin, Hengjie Song | These days, unsupervised super-resolution (SR) is soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i.e. first generating synthetic low-resolution (LR) images Y^g corresponding to real-world high-resolution (HR) images X^r in the real-world LR domain Y^r, and then utilizing the pseudo pairs Y^g, X^r for training in a supervised manner. Unfortunately, since image translation itself is an extremely challenging task, the SR performance of these approaches is severely limited by the domain gap between generated synthetic LR images and real LR images. In this paper, we propose a novel domain-distance aware super-resolution (DASR) approach for unsupervised real-world image SR. The domain gap between training data (e.g. Y^g) and testing data (e.g. Y^r) is addressed with our domain-gap aware training and domain-distance weighted supervision strategies. Domain-gap aware training takes additional benefit from real data in the target domain while domain-distance weighted supervision brings forward the more rational use of labeled source domain data. The proposed method is validated on synthetic and real datasets and the experimental results show that DASR consistently outperforms state-of-the-art unsupervised SR approaches in generating SR outputs with more realistic and natural textures. Codes are available at https://github.com/ShuhangGu/DASR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_Unsupervised_Real-World_Image_Super_Resolution_via_Domain-Distance_Aware_Training_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.01178 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Unsupervised_Real-World_Image_Super_Resolution_via_Domain-Distance_Aware_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Unsupervised_Real-World_Image_Super_Resolution_via_Domain-Distance_Aware_Training_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_Unsupervised_Real-World_Image_CVPR_2021_supplemental.pdf | null |
Learning to Track Instances without Video Annotations | Yang Fu, Sifei Liu, Umar Iqbal, Shalini De Mello, Humphrey Shi, Jan Kautz | Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fully-supervised methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Learning_to_Track_Instances_without_Video_Annotations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Learning_to_Track_Instances_without_Video_Annotations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Learning_to_Track_Instances_without_Video_Annotations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Learning_to_Track_CVPR_2021_supplemental.pdf | null |
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination | Xudong Wang, Ziwei Liu, Stella X. Yu | Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets. However, natural data could be highly correlated and long-tail distributed. Natural between-instance similarity conflicts with the presumed instance distinction, causing unstable training and poor performance. Our idea is to discover and integrate between-instance similarity into contrastive learning, not directly by instance grouping, but by cross-level discrimination (CLD) between instances and local instance groups. While invariant mapping of each instance is imposed by attraction within its augmented views, between-instance similarity emerges from common repulsion against instance groups. Our batch-wise and cross-view comparisons also greatly improve the positive/negative sample ratio of contrastive learning and achieve better invariant mapping. To effect both grouping and discrimination objectives, we impose them on features separately derived from a shared representation. In addition, we propose normalized projection heads and unsupervised hyper-parameter tuning for the first time. Our extensive experimentation demonstrates that CLD is a lean and powerful add-on to existing methods (e.g., NPID, MoCo, InfoMin, BYOL) on highly correlated, long-tail, or balanced datasets. It not only achieves new state-of-the-art on self-supervision, semi-supervision, and transfer learning benchmarks, but also beats MoCo v2 and SimCLR on every reported performance attained with a much larger compute. CLD effectively extends unsupervised learning to natural data and brings it closer to real-world applications. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.03813 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Feature_Learning_CVPR_2021_supplemental.pdf | null |
Representation Learning via Global Temporal Alignment and Cycle-Consistency | Isma Hadji, Konstantinos G. Derpanis, Allan D. Jepson | We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.05217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hadji_Representation_Learning_via_CVPR_2021_supplemental.zip | null |
Personalized Outfit Recommendation With Learnable Anchors | Zhi Lu, Yang Hu, Yan Chen, Bing Zeng | The multimedia community has recently seen a tremendous surge of interest in the fashion recommendation problem. A lot of efforts have been made to model the compatibility between fashion items. Some have also studied users' personal preferences for the outfits. There is, however, another difficulty in the task that hasn't been dealt with carefully by previous work. Users that are new to the system usually only have several (less than 5) outfits available for learning. With such a limited number of training examples, it is challenging to model the user's preferences reliably. In this work, we propose a new solution for personalized outfit recommendation that is capable of handling this case. We use a stacked self-attention mechanism to model the high-order interactions among the items. We then embed the items in an outfit into a single compact representation within the outfit space. To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i.e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes. We also learn a set of general anchors to model the general preference shared by all users. Based on this representation of the outfits and the users, we propose a simple but effective strategy for the new user profiling tasks. Extensive experiments on large scale real-world datasets demonstrate the performance of our proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Personalized_Outfit_Recommendation_With_Learnable_Anchors_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Personalized_Outfit_Recommendation_With_Learnable_Anchors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Personalized_Outfit_Recommendation_With_Learnable_Anchors_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Personalized_Outfit_Recommendation_CVPR_2021_supplemental.pdf | null |
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework | Zhizhong Huang, Junping Zhang, Hongming Shan | To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components---identity- and age-related features---through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, with a weight-sharing strategy to improve the age smoothness of synthesized faces. In addition, we collect and release a large cross-age face dataset with age and gender annotations to advance AIFR and FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace over state-of-the-art methods for AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild. The source code and dataset are available at https://github.com/Hzzone/MTLFace. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01520 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_When_Age-Invariant_Face_CVPR_2021_supplemental.pdf | null |
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking | Yiding Yang, Zhou Ren, Haoxiang Li, Chunluan Zhou, Xinchao Wang, Gang Hua | Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only then implementing data association and refinement. Despite the promising results achieved, such a strategy is inevitably prone to missed detections especially in heavily-cluttered scenes, since this tracking-by-detection paradigm is, by nature, largely dependent on visual evidences that are absent in the case of occlusion. In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion. Specifically, we derive this prediction of dynamics through a graph neural network (GNN) that explicitly accounts for both spatial-temporal and visual information. It takes as input the historical pose tracklets and directly predicts the corresponding poses in the following frame for each tracklet. The predicted poses will then be aggregated with the detected poses, if any, at the same frame so as to produce the final pose, potentially recovering the occluded joints missed by the estimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.03772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation | Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Wei Wang, Marco De Nadai | Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Smoothing_the_Disentangled_Latent_Style_Space_for_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.09016 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Smoothing_the_Disentangled_Latent_Style_Space_for_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Smoothing_the_Disentangled_Latent_Style_Space_for_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Smoothing_the_Disentangled_CVPR_2021_supplemental.pdf | null |
Robust Instance Segmentation Through Reasoning About Multi-Object Occlusion | Xiaoding Yuan, Adam Kortylewski, Yihong Sun, Alan Yuille | Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://github.com/XD7479/Multi-Object-Occlusion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Robust_Instance_Segmentation_Through_Reasoning_About_Multi-Object_Occlusion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Robust_Instance_Segmentation_Through_Reasoning_About_Multi-Object_Occlusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Robust_Instance_Segmentation_Through_Reasoning_About_Multi-Object_Occlusion_CVPR_2021_paper.html | CVPR 2021 | null | null |
Architectural Adversarial Robustness: The Case for Deep Pursuit | George Cazenavette, Calvin Murdock, Simon Lucey | Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deeper, real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cazenavette_Architectural_Adversarial_Robustness_The_Case_for_Deep_Pursuit_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cazenavette_Architectural_Adversarial_Robustness_The_Case_for_Deep_Pursuit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cazenavette_Architectural_Adversarial_Robustness_The_Case_for_Deep_Pursuit_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Scale Aligned Distillation for Low-Resolution Detection | Lu Qi, Jason Kuen, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, Jiaya Jia | In instance-level detection tasks (e.g., object detection), reducing input resolution is an easy option to improve runtime efficiency. However, this option severely hurts the detection performance. This paper focuses on boosting the performance of a low-resolution model, by distilling knowledge from a high/multi-resolution model. We first identify the challenge of applying knowledge distillation to teacher and student networks that act on different input resolutions. To tackle the challenge, we explore the idea of spatially aligning feature maps between models of different input resolutions, by shifting the position of the feature pyramid structure. With the alignment idea, we introduce aligned multi-scale training to train a multi-scale teacher that can distill its knowledge seamlessly to a low-resolution student. Furthermore, we propose cross feature-level fusion to dynamically fuse the multi-resolution features of the same teacher, to better guide the student. On several instance-level detection tasks and datasets, the low-resolution models trained via our approach perform competitively with high-resolution models trained via conventional multi-scale training, while outperforming the latter's low-resolution models by 2.1% to 3.6% in mAP. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qi_Multi-Scale_Aligned_Distillation_for_Low-Resolution_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Multi-Scale_Aligned_Distillation_for_Low-Resolution_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Multi-Scale_Aligned_Distillation_for_Low-Resolution_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qi_Multi-Scale_Aligned_Distillation_CVPR_2021_supplemental.pdf | null |
Deep Active Surface Models | Udaranga Wickramasinghe, Pascal Fua, Graham Knott | Active Surface Models have a long history of being useful to model complex 3D surfaces. But only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors at an acceptable computational cost. We will show that the resulting Deep Active Surface Models outperform equivalent architectures that use traditional regularization loss terms to impose smoothness priors for 3D surface reconstruction from 2D images and for 3D volume segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wickramasinghe_Deep_Active_Surface_Models_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08826 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wickramasinghe_Deep_Active_Surface_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wickramasinghe_Deep_Active_Surface_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wickramasinghe_Deep_Active_Surface_CVPR_2021_supplemental.zip | null |
Can We Characterize Tasks Without Labels or Features? | Bram Wallace, Ziyang Wu, Bharath Hariharan | The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task. Methods, however, generally depend on two separate assumptions: the presence of labeled images and access to powerful "probe" networks that yield useful features. In this work, we demonstrate the current reliance on both of these aspects and develop algorithms to operate when either of these assumptions fail. In the unlabeled case, we show that pseudolabels from the probe network provide discriminative enough gradients to perform nearly-equal task selection even when the probe network is trained on imagery unrelated to the tasks. To compute the embedding with no probe network at all, we introduce the Task Tangent Kernel (TTK) which uses a kernelized distance across multiple random networks to achieve performance over double that of other methods with randomly initialized models. Code is available at https://github.com/BramSW/task_characterization_cvpr_2021/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wallace_Can_We_Characterize_Tasks_Without_Labels_or_Features_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wallace_Can_We_Characterize_Tasks_Without_Labels_or_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wallace_Can_We_Characterize_Tasks_Without_Labels_or_Features_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wallace_Can_We_Characterize_CVPR_2021_supplemental.pdf | null |
Scene Essence | Jiayan Qiu, Yiding Yang, Xinchao Wang, Dacheng Tao | What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an end-to-end learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Scene_Essence_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Scene_Essence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Scene_Essence_CVPR_2021_paper.html | CVPR 2021 | null | null |
Visual Room Rearrangement | Luca Weihs, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi | There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen environments. In this paper, we propose a new dataset and baseline models for the task of Rearrangement. We particularly focus on the task of Room Rearrangement: an agent begins by exploring a room and recording objects' initial configurations. We then remove the agent and change the poses and states (e.g., open/closed) of some objects in the room. The agent must restore the initial configurations of all objects in the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement settings involving 72 different object types in 120 scenes. Our experiments show that solving this challenging interactive task that involves navigation and object interaction is beyond the capabilities of the current state-of-the-art techniques for embodied tasks and we are still very far from achieving perfect performance on these types of tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Weihs_Visual_Room_Rearrangement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16544 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Weihs_Visual_Room_Rearrangement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Weihs_Visual_Room_Rearrangement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Weihs_Visual_Room_Rearrangement_CVPR_2021_supplemental.pdf | null |
VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts | Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden | Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM). The model disentangles latent time-varying and dynamic factors via the incorporation of hierarchical structure with a dynamic prior and a Mixture of Experts decoder. VDSM learns separate disentangled representations for the identity of the object or person in the video, and for the action being performed. We evaluate VDSM across a range of qualitative and quantitative tasks including identity and dynamics transfer, sequence generation, Frechet Inception Distance, and factor classification. VDSM achieves state-of-the-art performance and exceeds adversarial methods, even when the methods use additional supervision. | https://openaccess.thecvf.com/content/CVPR2021/papers/Vowels_VDSM_Unsupervised_Video_Disentanglement_With_State-Space_Modeling_and_Deep_Mixtures_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07292 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Vowels_VDSM_Unsupervised_Video_Disentanglement_With_State-Space_Modeling_and_Deep_Mixtures_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Vowels_VDSM_Unsupervised_Video_Disentanglement_With_State-Space_Modeling_and_Deep_Mixtures_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vowels_VDSM_Unsupervised_Video_CVPR_2021_supplemental.pdf | null |
Rotation-Only Bundle Adjustment | Seong Hun Lee, Javier Civera | We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. When two calibrated cameras observe five or more of the same points, their relative rotation can be recovered independently of the translation. We extend this idea to multiple views, thereby decoupling the rotation estimation from the translation and structure estimation. Our approach provides several benefits such as complete immunity to inaccurate translations and structure, and the accuracy improvement when used with rotation averaging. We perform extensive evaluations on both synthetic and real datasets, demonstrating consistent and significant gains in accuracy when used with the state-of-the-art rotation averaging method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11724 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_supplemental.pdf | null |
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations | Wolfgang Stammer, Patrick Schramowski, Kristian Kersting | Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. These "visual" explanations are often insufficient, as the model's actual concept remains elusive. Moreover, without insights into the model's semantic concept, it is difficult --if not impossible-- to intervene on the model's behavior via its explanations, called Explanatory Interactive Learning. Consequently, we propose to intervene on a Neuro-Symbolic scene representation, which allows one to revise the model on the semantic level, e.g. "never focus on the color to make your decision". We compiled a novel con-founded visual scene data set, the CLEVR-Hans data set,capturing complex compositions of different objects. The results of our experiments on CLEVR-Hans demonstrate that our semantic explanations, i.e. compositional explanations at a per-object level, can identify confounders that are not identifiable using "visual" explanations only. More importantly, feedback on this semantic level makes it possible to revise the model from focusing on these factors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12854 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Stammer_Right_for_the_CVPR_2021_supplemental.pdf | null |
Polygonal Point Set Tracking | Gunhee Nam, Miran Heo, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim | In this paper, we propose a novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target contour, and our goal is to track corresponding points on the target contour. Those outputs enable us to apply various visual effects such as motion tracking, part deformation, and texture mapping. To this end, we propose a new method to track the corresponding points between frames by the global-local alignment with delicately designed losses and regularization terms. We also introduce a novel learning strategy using synthetic and VOS datasets that makes it possible to tackle the problem without developing the point correspondence dataset. Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods. In addition, we present visual-effects applications of our method on part distortion and text mapping. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nam_Polygonal_Point_Set_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.14584 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Polygonal_Point_Set_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Polygonal_Point_Set_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nam_Polygonal_Point_Set_CVPR_2021_supplemental.zip | null |
Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence | Yu Deng, Jiaolong Yang, Xin Tong | We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Deformed_Implicit_Field_Modeling_3D_Shapes_With_Learned_Dense_Correspondence_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13650 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Deformed_Implicit_Field_Modeling_3D_Shapes_With_Learned_Dense_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Deformed_Implicit_Field_Modeling_3D_Shapes_With_Learned_Dense_Correspondence_CVPR_2021_paper.html | CVPR 2021 | null | null |
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing | Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang | In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different network architectures. We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses. We further use the verified hypotheses to revise architectures of existing DNNs and improve their utilities. Experiments demonstrate the effectiveness of our method. The code will be released when this paper is accepted. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Verifiability_and_Predictability_Interpreting_Utilities_of_Network_Architectures_for_Point_CVPR_2021_paper.pdf | http://arxiv.org/abs/1911.09053 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Verifiability_and_Predictability_Interpreting_Utilities_of_Network_Architectures_for_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Verifiability_and_Predictability_Interpreting_Utilities_of_Network_Architectures_for_Point_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Verifiability_and_Predictability_CVPR_2021_supplemental.pdf | null |
Tracking Pedestrian Heads in Dense Crowd | Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand, Julien Pettre | Tracking humans in crowded video sequences is an important constituent of visual scene understanding. Increasing crowd density challenges visibility of humans, limiting the scalability of existing pedestrian trackers to higher crowd densities. For that reason, we propose to revitalize head tracking with Crowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with over 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For evaluation, we proposed a new metric, IDEucl, to measure an algorithm's efficacy in preserving a unique identity for the longest stretch in image coordinate space, thus building a correspondence between pedestrian crowd motion and the performance of a tracking algorithm. Moreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes. We extend HeadHunter with a Particle Filter and a color histogram based re-identification module for head tracking. To establish this as a strong baseline, we compare our tracker with existing state-of-the-art pedestrian trackers on CroHD and demonstrate superiority, especially in identity preserving tracking metrics. With a light-weight head detector and a tracker which is efficient at identity preservation, we believe our contributions will serve useful in advancement of pedestrian tracking in dense crowds. We make our dataset, code and models publicly available at https://project.inria.fr/crowdscience/project/dense-crowd-head-tracking/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sundararaman_Tracking_Pedestrian_Heads_in_Dense_Crowd_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13516 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sundararaman_Tracking_Pedestrian_Heads_in_Dense_Crowd_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sundararaman_Tracking_Pedestrian_Heads_in_Dense_Crowd_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sundararaman_Tracking_Pedestrian_Heads_CVPR_2021_supplemental.pdf | null |
Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks | Francis Williams, Matthew Trager, Joan Bruna, Denis Zorin | We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves state-of-the-art results, outperforming recent neural network-based techniques and widely used Poisson Surface Reconstruction (which, as we demonstrate, can also be viewed as a type of kernel method). Because our approach is based on a simple kernel formulation, it is easy to analyze and can be accelerated by general techniques designed for kernel-based learning. We provide explicit analytical expressions for our kernel and argue that our formulation can be seen as a generalization of cubic spline interpolation to higher dimensions. In particular, the RKHS norm associated with Neural Splines biases toward smooth interpolants. | https://openaccess.thecvf.com/content/CVPR2021/papers/Williams_Neural_Splines_Fitting_3D_Surfaces_With_Infinitely-Wide_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.13782 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Williams_Neural_Splines_Fitting_3D_Surfaces_With_Infinitely-Wide_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Williams_Neural_Splines_Fitting_3D_Surfaces_With_Infinitely-Wide_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Williams_Neural_Splines_Fitting_CVPR_2021_supplemental.pdf | null |
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation | Bin Yan, Xinyu Zhang, Dong Wang, Huchuan Lu, Xiaoyun Yang | Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation. These methods first coarsely locate the target and then refine the initial prediction in the following stages. However, existing approaches still suffer from limited precision, and the coupling of different stages severely restricts the method's transferability. This work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers' box estimation quality. By exploring a series of design options, we conclude that the key to successful refinement is extracting and maintaining detailed spatial information as much as possible. Following this principle, Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an auxiliary mask head as the core components. Comprehensive experiments on TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers show that our approach significantly improves the base trackers' performance with little extra latency. The proposed Alpha-Refine method leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp) and the ARDiMP50 (ARstrengthened DiMP50) achieve good efficiency-precision trade-off, while the ARDiMPsuper (AR strengthened DiMP-super) achieves very competitive performance at a real-time speed. Code and pretrained models are available at https://github.com/MasterBin-IIAU/AlphaRefine. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Alpha-Refine_Boosting_Tracking_Performance_by_Precise_Bounding_Box_Estimation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Alpha-Refine_Boosting_Tracking_Performance_by_Precise_Bounding_Box_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Alpha-Refine_Boosting_Tracking_Performance_by_Precise_Bounding_Box_Estimation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval | Yang Liu, Qingchao Chen, Samuel Albanie | In this paper, we study the task of visual-text retrieval in the highly practical setting in which labelled visual data with paired text descriptions are available in one domain (the "source"), but only unlabelled visual data (without text descriptions) are available in the domain of interest (the "target"). We propose the ADAPTIVE CROSS-MODAL PROTOTYPES framework which seeks to enable target domain retrieval by learning cross-modal visual-text representations while minimising both uni-modal and cross-modal distribution shift across the source and target domains. Our approach is built upon two key ideas: first, we encode the inductive bias that the learned cross-modal representations should be compositional with respect to concepts in each modality--this is achieved through clustering pretrained uni-modal features across each domain and designing a careful regularisation scheme to preserve the resulting structure. Second, we employ mutual information maximisation between cross-modal representations in the source and target domains during learning--this provides a mechanism that preserves commonalities between the domains while discarding signal in each that cannot be inferred from the other. We showcase our approach for the task of cross-domain visual-text retrieval, outperforming existing approaches for both images and videos. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Adaptive_Cross-Modal_Prototypes_for_Cross-Domain_Visual-Language_Retrieval_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Cross-Modal_Prototypes_for_Cross-Domain_Visual-Language_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Cross-Modal_Prototypes_for_Cross-Domain_Visual-Language_Retrieval_CVPR_2021_paper.html | CVPR 2021 | null | null |
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts | Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut | The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pre-training data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [Sharma et al. 2018] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.08981 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Changpinyo_Conceptual_12M_Pushing_CVPR_2021_supplemental.pdf | null |
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data | Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong | Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision. Our implementation is available at https://github.com/jw9730/setvae. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15619 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_SetVAE_Learning_Hierarchical_CVPR_2021_supplemental.pdf | null |
Few-Shot 3D Point Cloud Semantic Segmentation | Na Zhao, Tat-Seng Chua, Gim Hee Lee | Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training. To mitigate these limitations, we propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method to segment new classes given a few labeled examples. Specifically, each class is represented by multiple prototypes to model the complex data distribution of labeled points. Subsequently, we employ a transductive label propagation method to exploit the affinities between labeled multi-prototypes and unlabeled points, and among the unlabeled points. Furthermore, we design an attention-aware multi-level feature learning network to learn the discriminative features that capture the geometric dependencies and semantic correlations between points. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings (i.e., 2/3-way 1/5-shot) on two benchmark datasets. Our code is available at https://github.com/Na-Z/attMPTI. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Few-Shot_3D_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.12052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Few-Shot_3D_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Few-Shot_3D_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Few-Shot_3D_Point_CVPR_2021_supplemental.pdf | null |
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching | Zhelun Shen, Yuchao Dai, Zhibo Rao | Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models. In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. First, we propose a fused cost volume representation to deal with the large domain difference. By fusing multiple low-resolution dense cost volumes to enlarge the receptive field, we can extract robust structural representations for initial disparity estimation. Second, we propose a cascade cost volume representation to alleviate the unbalanced disparity distribution. Specifically, we employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space, in this way driving the network progressively prune out the space of unlikely correspondences. By iteratively narrowing down the disparity search space and improving the cost volume resolution, the disparity estimation is gradually refined in a coarse-to-fine manner. When trained on the same training images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed model parameters and hyperparameters, our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust Vision Challenge 2020. The code will be available at https://github.com/gallenszl/CFNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_CFNet_Cascade_and_Fused_Cost_Volume_for_Robust_Stereo_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04314 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_CFNet_Cascade_and_Fused_Cost_Volume_for_Robust_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_CFNet_Cascade_and_Fused_Cost_Volume_for_Robust_Stereo_Matching_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_CFNet_Cascade_and_CVPR_2021_supplemental.zip | null |
Adaptive Consistency Prior Based Deep Network for Image Denoising | Chao Ren, Xiaohai He, Chuncheng Wang, Zhibo Zhao | Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network. First, by introducing a non-linear filtering operator, a reliability matrix, and a high-dimensional feature transformation function into the traditional consistency prior, we propose a novel adaptive consistency prior (ACP). Second, by incorporating the ACP term into the maximum a posteriori framework, a model-based denoising method is proposed. This method is further used to inform the network design, leading to a novel end-to-end trainable and interpretable deep denoising network, called DeamNet. Note that the unfolding process leads to a promising module called dual element-wise attention mechanism (DEAM) module. To the best of our knowledge, both our ACP constraint and DEAM module have not been reported in the previous literature. Extensive experiments verify the superiority of DeamNet on both synthetic and real noisy image datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ren_Adaptive_Consistency_Prior_CVPR_2021_supplemental.pdf | null |
Topological Planning With Transformers for Vision-and-Language Navigation | Kevin Chen, Junshen K. Chen, Jo Chuang, Marynel Vazquez, Silvio Savarese | Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predict a navigation plan in the map. The plan is then executed with low-level actions (e.g. forward, rotate) using a robust controller. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Topological_Planning_With_Transformers_for_Vision-and-Language_Navigation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05292 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Topological_Planning_With_Transformers_for_Vision-and-Language_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Topological_Planning_With_Transformers_for_Vision-and-Language_Navigation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Topological_Planning_With_CVPR_2021_supplemental.zip | null |
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation | Jaemin Na, Heechul Jung, Hyung Jin Chang, Wonjun Hwang | Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidence-based learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017. | https://openaccess.thecvf.com/content/CVPR2021/papers/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.09230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Generalized Few-Shot Object Detection Without Forgetting | Zhibo Fan, Yuchen Ma, Zeming Li, Jian Sun | Learning object detection from few examples recently emerged to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we claim that the ability to detect all classes is crucial as test samples may contain any instances in realistic applications, which requires the few-shot detector to learn new concepts without forgetting. Through analysis on transfer learning based methods, some neglected but beneficial properties are utilized to design a simple yet effective few-shot detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the pretrained RPN and Re-detector to find few-shot class objects without forgetting previous knowledge. Extensive experiments on few-shot detection benchmarks show that Retentive R-CNN significantly outperforms state-of-the-art methods on overall performance among all settings as it can achieve competitive results on few-shot classes and does not degrade on base class performance at all. Our approach has demonstrated that the long desired never-forgetting learner is available in object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.09491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fan_Generalized_Few-Shot_Object_CVPR_2021_supplemental.pdf | null |
Truly Shift-Invariant Convolutional Neural Networks | Anadi Chaman, Ivan Dokmanic | Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the output of a CNN can change significantly with small shifts in input--a problem caused by the presence of downsampling (stride) layers. The existing solutions rely either on data augmentation or on anti-aliasing, both of which have limitations and neither of which enables perfect shift invariance. Additionally, the gains obtained from these methods do not extend to image patterns not seen during training. To address these challenges, we propose adaptive polyphase sampling (APS), a simple sub-sampling scheme that allows convolutional neural networks to achieve 100% consistency in classification performance under shifts, without any loss in accuracy. With APS, the networks exhibit perfect consistency to shifts even before training, making it the first approach that makes convolutional neural networks truly shift-invariant. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14214 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chaman_Truly_Shift-Invariant_Convolutional_CVPR_2021_supplemental.pdf | null |
Leveraging the Availability of Two Cameras for Illuminant Estimation | Abdelrahman Abdelhamed, Abhijith Punnappurath, Michael S. Brown | Most modern smartphones are now equipped with two rear-facing cameras -- a main camera for standard imaging and an additional camera to provide wide-angle or telephoto zoom capabilities. In this paper, we leverage the availability of these two cameras for the task of illumination estimation using a small neural network to perform the illumination prediction. Specifically, if the two cameras' sensors have different spectral sensitivities, the two images provide different spectral measurements of the physical scene. A linear 3x3 color transform that maps between these two observations -- and that is unique to a given scene illuminant -- can be used to train a lightweight neural network comprising no more than 1460 parameters to predict the scene illumination. We demonstrate that this two-camera approach with a lightweight network provides results on par or better than much more complicated illuminant estimation methods operating on a single image. We validate our method's effectiveness through extensive experiments on radiometric data, a quasi-real two-camera dataset we generated from an existing single camera dataset, as well as a new real image dataset that we captured using a smartphone with two rear-facing cameras. | https://openaccess.thecvf.com/content/CVPR2021/papers/Abdelhamed_Leveraging_the_Availability_of_Two_Cameras_for_Illuminant_Estimation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Abdelhamed_Leveraging_the_Availability_of_Two_Cameras_for_Illuminant_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Abdelhamed_Leveraging_the_Availability_of_Two_Cameras_for_Illuminant_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Abdelhamed_Leveraging_the_Availability_CVPR_2021_supplemental.pdf | null |
LiDAR-Based Panoptic Segmentation via Dynamic Shifting Network | Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu | With the rapid advances of autonomous driving, it becomes critical to equip its sensing system with more holistic 3D perception. However, existing works focus on parsing either the objects (e.g. cars and pedestrians) or scenes (e.g. trees and buildings) from the LiDAR sensor. In this work, we address the task of LiDAR-based panoptic segmentation, which aims to parse both objects and scenes in a unified manner. As one of the first endeavors towards this new challenging task, we propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. In particular, DS-Net has three appealing properties: 1) strong backbone design. DS-Net adopts the cylinder convolution that is specifically designed for LiDAR point clouds. The extracted features are shared by the semantic branch and the instance branch which operates in a bottom-up clustering style. 2) Dynamic Shifting for complex point distributions. We observe that commonly-used clustering algorithms like BFS or DBSCAN are incapable of handling complex autonomous driving scenes with non-uniform point cloud distributions and varying instance sizes. Thus, we present an efficient learnable clustering module, dynamic shifting, which adapts kernel functions on-the-fly for different instances. 3) Consensus-driven Fusion. Finally, consensus-driven fusion is used to deal with the disagreement between semantic and instance predictions. To comprehensively evaluate the performance of LiDAR-based panoptic segmentation, we construct and curate benchmarks from two large-scale autonomous driving LiDAR datasets, SemanticKITTI and nuScenes. Extensive experiments demonstrate that our proposed DS-Net achieves superior accuracies over current state-of-the-art methods. Notably, we achieve 1st place on the public leaderboard of SemanticKITTI, outperforming 2nd place by 2.6% in terms of the PQ metric. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_LiDAR-Based_Panoptic_Segmentation_via_Dynamic_Shifting_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_LiDAR-Based_Panoptic_Segmentation_via_Dynamic_Shifting_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_LiDAR-Based_Panoptic_Segmentation_via_Dynamic_Shifting_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_LiDAR-Based_Panoptic_Segmentation_CVPR_2021_supplemental.pdf | null |
Towards Accurate 3D Human Motion Prediction From Incomplete Observations | Qiongjie Cui, Huaijiang Sun | Predicting accurate and realistic future human poses from historically observed sequences is a fundamental task in the intersection of computer vision, graphics, and artificial intelligence. Recently, continuous efforts have been devoted to addressing this issue, which has achieved remarkable progress. However, the existing work is seriously limited by complete observation, that is, once the historical motion sequence is incomplete (with missing values), it can only produce unexpected predictions or even deformities. Furthermore, due to inevitable reasons such as occlusion and the lack of equipment precision, the incompleteness of motion data occurs frequently, which hinders the practical application of current algorithms. In this work, we first notice this challenging problem, i.e., how to generate high-fidelity human motion predictions from incomplete observations. To solve it, we propose a novel multi-task graph convolutional network (MT-GCN). Specifically, the model involves two branches, in which the primary task is to focus on forecasting future 3D human actions accurately, while the auxiliary one is to repair the missing value of the incomplete observation. Both of them are integrated into a unified framework to share the spatio-temporal representation, which improves the final performance of each collaboratively. On three large-scale datasets, for various data missing scenarios in the real world, extensive experiments demonstrate that our approach is consistently superior to the state-of-the-art methods in which the missing values from incomplete observations are not explicitly analyzed. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cui_Towards_Accurate_3D_Human_Motion_Prediction_From_Incomplete_Observations_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Towards_Accurate_3D_Human_Motion_Prediction_From_Incomplete_Observations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Towards_Accurate_3D_Human_Motion_Prediction_From_Incomplete_Observations_CVPR_2021_paper.html | CVPR 2021 | null | null |
SiamMOT: Siamese Multi-Object Tracking | Bing Shuai, Andrew Berneshawi, Xinyu Li, Davide Modolo, Joseph Tighe | In this work, we focus on improving online multi-object tracking (MOT). In particular, we propose a novel region-based Siamese Multi-Object Tracking network, which we name SiamMOT. SiamMOT is based upon Faster-RCNN and adds a forward tracker that models the instance's motion across two frames such that detected instances can be associated in an online fashion. We present two variants of this tracker, an implicit motion model and a novel Siamese-type explicit motion model. We carry out extensive quantitative experiments on three important MOT datasets: MOT17, TAO-person and Caltech Roadside Pedestrians, showing the importance of motion modelling for MOT and the ability of SiamMOT to substantially outperform the state-of-the-art. Finally, SiamMOT also outperforms the winners of ACM MM'20 HiEve Grand Challenge on the Human in Events dataset. Moreover, SiamMOT is efficient, and it runs at 17 FPS for 720P videos on a single modern GPU. We will release SiamMOT source code upon acceptance of this paper. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.11595 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shuai_SiamMOT_Siamese_Multi-Object_CVPR_2021_supplemental.pdf | null |
Open-Book Video Captioning With Retrieve-Copy-Generate Network | Ziqi Zhang, Zhongang Qi, Chunfeng Yuan, Ying Shan, Bing Li, Ying Deng, Weiming Hu | In this paper, we convert traditional video captioning task into a new paradigm, i.e., Open-book Video Captioning, which generates natural language under the prompts of video-content-relevant sentences, not limited to the video itself. To address the open-book video captioning problem, we propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is leveraged to effectively retrieve sentences as hints from the training corpus, and a copy-mechanism generator is introduced to dynamically extract expressions from multi-retrievals. The two modules can be trained end-to-end or separately which is flexible and extensible. Our framework coordinates the conventional retrieval based methods with orthodox encoder-decoder methods, which can not only draw on the diverse expressions in the retrieved sentences but also generate natural and accurate content of the video. Extensive experiments on several benchmark datasets show that our proposed approach performs better than state-of-the-art approaches, indicating the effectiveness and promising of the proposed paradigm in the task of video captioning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Open-Book_Video_Captioning_With_Retrieve-Copy-Generate_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05284 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Open-Book_Video_Captioning_With_Retrieve-Copy-Generate_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Open-Book_Video_Captioning_With_Retrieve-Copy-Generate_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Open-Book_Video_Captioning_CVPR_2021_supplemental.pdf | null |
MUST-GAN: Multi-Level Statistics Transfer for Self-Driven Person Image Generation | Tianxiang Ma, Bo Peng, Wei Wang, Jing Dong | Pose-guided person image generation usually involves using paired source-target images to supervise the training, which significantly increases the data preparation effort and limits the application of the models. To deal with this problem, we propose a novel multi-level statistics transfer model, which disentangles and transfers multi-level appearance features from person images and merges them with pose features to reconstruct the source person images themselves. So that the source images can be used as supervision for self-driven person image generation. Specifically, our model extracts multi-level features from the appearance encoder and learns the optimal appearance representation through attention mechanism and attributes statistics. Then we transfer them to a pose-guided generator for re-fusion of appearance and pose. Our approach allows for flexible manipulation of person appearance and pose properties to perform pose transfer and clothes style transfer tasks. Experimental results on the DeepFashion dataset demonstrate our method's superiority compared with state-of-the-art supervised and unsupervised methods. In addition, our approach also performs well in the wild. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_MUST-GAN_Multi-Level_Statistics_Transfer_for_Self-Driven_Person_Image_Generation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_MUST-GAN_Multi-Level_Statistics_Transfer_for_Self-Driven_Person_Image_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_MUST-GAN_Multi-Level_Statistics_Transfer_for_Self-Driven_Person_Image_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_MUST-GAN_Multi-Level_Statistics_CVPR_2021_supplemental.pdf | null |
Learning Camera Localization via Dense Scene Matching | Shitao Tang, Chengzhou Tang, Rui Huang, Siyu Zhu, Ping Tan | Camera localization aims to estimate 6 DoF camera poses from RGB images. Traditional methods detect and match interest points between a query image and a pre-built 3D model. Recent learning-based approaches encode scene structures into a specific convolutional neural network(CNN) and thus are able to predict dense coordinates from RGB images. However, most of them require re-training or re-adaption for a new scene and have difficulties in handling large-scale scenes due to limited network capacity. We present a new method for scene agnostic camera localization using dense scene matching (DSM), where the cost volume is constructed between a query image and a scene. The cost volume and the corresponding coordinates are processed by a CNN to predict dense coordinates. Camera poses can then be solved by PnP algorithms. In addition, our method can be extended to temporal domain, giving extra performance boost during testing time. Our scene-agnostic approach achieves comparable accuracy as the existing scene-specific approaches on the 7scenes and Cambridge benchmark. This approach also remarkably outperforms state-of-the-art scene-agnostic dense coordinate regression network SANet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Learning_Camera_Localization_via_Dense_Scene_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16792 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Learning_Camera_Localization_via_Dense_Scene_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Learning_Camera_Localization_via_Dense_Scene_Matching_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Learning_Camera_Localization_CVPR_2021_supplemental.pdf | null |
SDD-FIQA: Unsupervised Face Image Quality Assessment With Similarity Distribution Distance | Fu-Zhao Ou, Xingyu Chen, Ruixin Zhang, Yuge Huang, Shaoxin Li, Jilin Li, Yong Li, Liujuan Cao, Yuan-Gen Wang | In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability of the face image. Most previous works aim to estimate the sample-wise embedding uncertainty or pair-wise similarity as the quality score, which only considers the partial information from the intra-class. However, these methods ignore the valuable information from the inter-class, which is for estimating the recognizability of face image. In this work, we argue that a high-quality face image should be similar to its intra-class samples and dissimilar to its inter-class samples. Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA). Our method generates quality pseudo-labels by calculating the Wasserstein Distance (WD) between the intra-class and inter-class similarity distributions. With these quality pseudo-labels, we are capable of training a regression network for quality prediction. Extensive experiments on benchmark datasets demonstrate that the proposed SDD-FIQA surpasses the state-of-the-arts by an impressive margin. Meanwhile, our method shows good generalization across different recognition systems. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.html | CVPR 2021 | null | null |
Self-Aligned Video Deraining With Transmission-Depth Consistency | Wending Yan, Robby T. Tan, Wenhan Yang, Dengxin Dai | In this paper, we address the problems of rain streaks and rain accumulation removal in video, by developing a self-aligned network with transmission-depth consistency. Existing video based deraining method focus only on rain streak removal, and commonly use optical flow to align the rain video frames. However, besides rain streaks, rain accummulation can considerably degrade visibility; and, optical flow estimation in a rain video is still erroneous, making the deraining performance tend to be inaccurate. Our method employs deformable convolution layers in our encoder to achieve feature-level frame alignment, and hence avoids using optical flow. For rain streaks, our method predicts the current frame from its adjacent frames, such that rain streaks that appear randomly in the temporal domain can be removed. For rain accumulation, our method employs transmission-depth consistency to resolve the ambiguity between the depth and water-droplet density. Our network estimates the depth from consecutive rain-accumulation-removal outputs, and we calculate the transmission map using a commonly used physics model. To ensure photometric-temporal and depth-temporal consistencies, our network also estimate the camera poses, so that we can warp one frame to its adjacent frames. Experimental results show that our method is effective in removing both rain streaks and rain accumulation. Our results outperform those of state-of-the-art methods quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Self-Aligned_Video_Deraining_With_Transmission-Depth_Consistency_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Self-Aligned_Video_Deraining_With_Transmission-Depth_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Self-Aligned_Video_Deraining_With_Transmission-Depth_Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_Self-Aligned_Video_Deraining_CVPR_2021_supplemental.pdf | null |
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning | Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, Zheng-Jun Zha | Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype learning scheme. Our scheme consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new class by explicitly considering the dependencies among different classes. Particularly, a dynamic relation projection module is proposed to calculate the relation matrix in a shared embedding space and leverage it as the factor for bootstrapping the update of prototypes. Extensive experiments on three benchmark datasets demonstrate the above-par incremental performance, outperforming state-of-the-art methods by a margin of 13%, 17% and 11%, respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation | Tal Reiss, Niv Cohen, Liron Bergman, Yedid Hoshen | Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pre-trained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pre-trained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods. In order to obtain further performance gains in anomaly detection, we adapt pre-trained features to the target distribution. Although transfer learning methods are well established in multi-class classification problems, the one-class classification (OCC) setting is not as well explored. It turns out that naive adaptation methods, which typically work well in supervised learning, often result in catastrophic collapse (feature deterioration) and reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates using specialized architectures, but this limits the adaptation performance gain. We propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. Our method, PANDA, outperforms the state-of-the-art in the OCC, outlier exposure and anomaly segmentation settings by large margins. | https://openaccess.thecvf.com/content/CVPR2021/papers/Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.05903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reiss_PANDA_Adapting_Pretrained_CVPR_2021_supplemental.pdf | null |
Towards Compact CNNs via Collaborative Compression | Yuchao Li, Shaohui Lin, Jianzhuang Liu, Qixiang Ye, Mengdi Wang, Fei Chao, Fan Yang, Jincheng Ma, Qi Tian, Rongrong Ji | Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in an isolated manner, leading to significant accuracy drop when pursuing high compression rates. In this paper, we propose a Collaborative Compression (CC) scheme, which joints channel pruning and tensor decomposition to compress CNN models by simultaneously learning the model sparsity and low-rankness. Specifically, we first investigate the compression sensitivity of each layer in the network, and then propose a Global Compression Rate Optimization that transforms the decision problem of compression rate into an optimization problem. After that, we propose multi-step heuristic compression to remove redundant compression units step-by-step, which fully considers the effect of the remaining compression space (i.e., unremoved compression units). Our method demonstrates superior performance gains over previous ones on various datasets and backbone architectures. For example, we achieve 52.9% FLOPs reduction by removing 48.4% parameters on ResNet-50 with only a Top-1 accuracy drop of 0.56% on ImageNet 2012. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.11228 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Towards_Compact_CNNs_CVPR_2021_supplemental.pdf | null |
Embracing Uncertainty: Decoupling and De-Bias for Robust Temporal Grounding | Hao Zhou, Chongyang Zhang, Yan Luo, Yanjun Chen, Chuanping Hu | Temporal grounding aims to localize temporal boundaries within untrimmed videos by language queries, but it faces the challenge of two types of inevitable human uncertainties: query uncertainty and label uncertainty. The two uncertainties stem from human subjectivity, leading to limited generalization ability of temporal grounding. In this work, we propose a novel DeNet (Decoupling and De-bias) to embrace human uncertainty: Decoupling -- We explicitly disentangle each query into a relation feature and a modified feature. The relation feature, which is mainly based on skeleton-like words (including nouns and verbs), aims to extract basic and consistent information in the presence of query uncertainty. Meanwhile, modified feature assigned with style-like words (including adjectives, adverbs, etc) represents the subjective information, and thus brings personalized predictions; De-bias -- We propose a de-bias mechanism to generate diverse predictions, aim to alleviate the bias caused by single-style annotations in the presence of label uncertainty. Moreover, we put forward new multi-label metrics to diversify the performance evaluation. Extensive experiments show that our approach is more effective and robust than state-of-the-arts on Charades-STA and ActivityNet Captions datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Embracing_Uncertainty_Decoupling_and_De-Bias_for_Robust_Temporal_Grounding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16848 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Embracing_Uncertainty_Decoupling_and_De-Bias_for_Robust_Temporal_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Embracing_Uncertainty_Decoupling_and_De-Bias_for_Robust_Temporal_Grounding_CVPR_2021_paper.html | CVPR 2021 | null | null |
Separating Skills and Concepts for Novel Visual Question Answering | Spencer Whitehead, Hui Wu, Heng Ji, Rogerio Feris, Kate Saenko | Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question, such as objects and people. VQA methods should be able to compose skills and concepts in novel ways, regardless of whether the specific composition has been seen in training, yet we demonstrate that existing models have much to improve upon towards handling new compositions. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. We enforce these properties with a novel contrastive learning procedure that does not rely on external annotations and can be learned from unlabeled image-question pairs. Experiments demonstrate the effectiveness of our approach for improving compositional and grounding performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Whitehead_Separating_Skills_and_Concepts_for_Novel_Visual_Question_Answering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Whitehead_Separating_Skills_and_Concepts_for_Novel_Visual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Whitehead_Separating_Skills_and_Concepts_for_Novel_Visual_Question_Answering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Whitehead_Separating_Skills_and_CVPR_2021_supplemental.pdf | null |
Discrete-Continuous Action Space Policy Gradient-Based Attention for Image-Text Matching | Shiyang Yan, Li Yu, Yuan Xie | Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely used in image-text matching models does not have supervision. We propose a novel attention scheme which projects the image and text embedding into a common space and optimises the attention weights directly towards the evaluation metrics. The proposed attention scheme can be considered as a kind of supervised attention and requiring no additional annotations. It is trained via a novel Discrete-continuous action space policy gradient algorithm, which is more effective in modelling complex action space than previous continuous action space policy gradient. We evaluate the proposed methods on two widely-used benchmark datasets: Flickr30k and MS-COCO, outperforming the previous approaches by a large margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Discrete-Continuous_Action_Space_Policy_Gradient-Based_Attention_for_Image-Text_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Discrete-Continuous_Action_Space_Policy_Gradient-Based_Attention_for_Image-Text_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Discrete-Continuous_Action_Space_Policy_Gradient-Based_Attention_for_Image-Text_Matching_CVPR_2021_paper.html | CVPR 2021 | null | null |
Scalable Differential Privacy With Sparse Network Finetuning | Zelun Luo, Daniel J. Wu, Ehsan Adeli, Li Fei-Fei | We propose a novel method for privacy-preserving training of deep neural networks leveraging public, out-domain data. While differential privacy (DP) has emerged as a mechanism to protect sensitive data in training datasets, its application to complex visual recognition tasks remains challenging. Traditional DP methods, such as Differentially-Private Stochastic Gradient Descent (DP-SGD), only perform well on simple datasets and shallow networks, while recent transfer learning-based DP methods often make unrealistic assumptions about the availability and distribution of public data. In this work, we argue that minimizing the number of trainable parameters is the key to improving the privacy-performance tradeoff of DP on complex visual recognition tasks. We also propose a novel transfer learning paradigm that finetunes a very sparse subnetwork with DP, inspired by this argument. We conduct extensive experiments and ablation studies on two visual recognition tasks: CIFAR-100 -> CIFAR-10 (standard DP setting) and the CD-FSL challenge (few-shot, multiple levels of domain shifts) and demonstrate competitive experimental performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Scalable_Differential_Privacy_With_Sparse_Network_Finetuning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Scalable_Differential_Privacy_With_Sparse_Network_Finetuning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Scalable_Differential_Privacy_With_Sparse_Network_Finetuning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Video Object Segmentation Using Global and Instance Embedding Learning | Wenbin Ge, Xiankai Lu, Jianbing Shen | In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective. The current VOS task involves two main challenges: object instance differentiation and cross-frame instance alignment. Most state-of-the-art matching based VOS methods simplify this task into a binary segmentation task and tackle each instance independently. In contrast, we decompose the VOS task into two subtasks: global embedding learning that segments foreground objects of each frame in a pixel-to-pixel manner, and instance feature embedding learning that separates instances. The outputs of these two subtasks are fused to obtain the final instance masks quickly and accurately. Through using the relation among different instances per-frame as well as temporal relation across different frames, the proposed network learns to differentiate multiple instances and associate them properly in one feed-forward manner. Extensive experimental results on the challenging DAVIS and Youtube-VOS datasets show that our method achieves better performances than most counterparts in each case. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Video_Object_Segmentation_Using_Global_and_Instance_Embedding_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Video_Object_Segmentation_Using_Global_and_Instance_Embedding_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Video_Object_Segmentation_Using_Global_and_Instance_Embedding_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_Video_Object_Segmentation_CVPR_2021_supplemental.pdf | null |
Scene Text Retrieval via Joint Text Detection and Similarity Learning | Hao Wang, Xiang Bai, Mingkun Yang, Shenggao Zhu, Jing Wang, Wenyu Liu | Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar with a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by directly learning a cross-modal similarity between a query text and each text instance from natural images. Specifically, we establish an end-to-end trainable network, jointly optimizing the procedures of scene text detection and cross-modal similarity learning. In this way, scene text retrieval can be simply performed by ranking the detected text instances with the learned similarity. Experiments on three benchmark datasets demonstrate our method consistently outperforms the state-of-the-art scene text spotting/retrieval approaches. In particular, the proposed framework of joint detection and similarity learning achieves significantly better performance than separated methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Scene_Text_Retrieval_CVPR_2021_supplemental.pdf | null |
Learning Continuous Image Representation With Local Implicit Image Function | Yinbo Chen, Sifei Liu, Xiaolong Wang | How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09161 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper.html | CVPR 2021 | null | null |
Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration | Shaofei Wang, Andreas Geiger, Siyu Tang | Registering point clouds of dressed humans to parametric human models is a challenging task in computer vision. Traditional approaches often rely on heavily engineered pipelines that require accurate manual initialization of human poses and tedious post-processing. More recently, learning-based methods are proposed in hope to automate this process. We observe that pose initialization is key to accurate registration but existing methods often fail to provide accurate pose initialization. One major obstacle is that, despite recent effort on rotation representation learning in neural networks, regressing joint rotations from point clouds or images of humans is still very challenging. To this end, we propose novel piecewise transformation fields (PTF), a set of functions that learn 3D translation vectors to map any query point in posed space to its correspond position in rest-pose space. We combine PTF with multi-class occupancy networks, obtaining a novel learning-based framework that learns to simultaneously predict shape and per-point correspondences between the posed space and the canonical space for clothed human. Our key insight is that the translation vector for each query point can be effectively estimated using the point-aligned local features; consequently, rigid per bone transformations and joint rotations can be obtained efficiently via a least-square fitting given the estimated point correspondences, circumventing the challenging task of directly regressing joint rotations from neural networks. Furthermore, the proposed PTF facilitate canonicalized occupancy estimation, which greatly improves generalization capability and result in more accurate surface reconstruction with only half of the parameters compared with the state-of-the-art. Both qualitative and quantitative studies show that fitting parametric models with poses initialized by our network results in much better registration quality, especially for extreme poses. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Locally_Aware_Piecewise_Transformation_Fields_for_3D_Human_Mesh_Registration_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Locally_Aware_Piecewise_Transformation_Fields_for_3D_Human_Mesh_Registration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Locally_Aware_Piecewise_Transformation_Fields_for_3D_Human_Mesh_Registration_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Locally_Aware_Piecewise_CVPR_2021_supplemental.pdf | null |
Graph Attention Tracking | Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, Chunhua Shen | Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature region needs to be pre-fixed, these cross-correlation base methods suffer from either reserving much adverse background information or missing a great deal of foreground information. Moreover, the global matching between the target and search region also largely neglects the target structure and part-level information. In this paper, to solve the above issues, we propose a simple target-aware Siamese graph attention network for general object tracking. We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature. Further, instead of using the pre-fixed region cropping for template-feature-area selection, we investigate a target-aware area selection mechanism to fit the size and aspect ratio variations of different objects. Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art trackers and achieves leading performance. Code is available at: https://git.io/SiamGAT | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Graph_Attention_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Graph_Attention_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Graph_Attention_Tracking_CVPR_2021_paper.html | CVPR 2021 | null | null |
ReDet: A Rotation-Equivariant Detector for Aerial Object Detection | Jiaming Han, Jian Ding, Nan Xue, Gui-Song Xia | Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model the orientation variation, large amounts of rotation augmented data is needed to train an accurate object detector. In this paper, we propose a Rotation-equivariant Detector (ReDet) to address these issues, which explicitly encodes rotation equivariance and rotation invariance. More precisely, we incorporate rotation-equivariant networks into the detector to extract rotation-equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size. Based on the rotation-equivariant features, we also present Rotation-invariant RoI Align (RiRoI Align), which adaptively extracts rotation-invariant features from equivariant features according to the orientation of RoI. Extensive experiments on several challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that our method can achieve state-of-the-art performance on the task of aerial object detection. Compared with previous best results, our ReDet gains 1.2, 3.5 and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing the number of parameters by 60% (313 Mb vs. 121 Mb). The code is available at: https://github.com/csuhan/ReDet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07733 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Action Shuffle Alternating Learning for Unsupervised Action Segmentation | Jun Li, Sinisa Todorovic | This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and action-level structure of videos. Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding. The positive and negative sequences consist of action segments sampled from videos, where in the former the sampled action segments respect their time ordering in the video, and in the latter they are shuffled. As supervision of actions is not available and our SSL requires access to action segments, we specify an HMM that explicitly models action lengths, and infer a MAP action segmentation with the Viterbi algorithm. The resulting action segmentation is used as pseudo-ground truth for estimating our action-level feature embedding and updating the HMM. We alternate the above steps within the Generalized EM framework, which ensures convergence. Our evaluation on the Breakfast, YouTube Instructions, and 50Salads datasets gives superior results to those of the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Action_Shuffle_Alternating_Learning_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Action_Shuffle_Alternating_Learning_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Action_Shuffle_Alternating_Learning_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences | Fengmao Lv, Xiang Chen, Yanyong Huang, Lixin Duan, Guosheng Lin | Human multimodal emotion recognition involves time-series data of different modalities, such as natural language, visual motions, and acoustic behaviors. Due to the variable sampling rates for sequences from different modalities, the collected multimodal streams are usually unaligned. The asynchrony across modalities increases the difficulty on conducting efficient multimodal fusion. Hence, this work mainly focuses on multimodal fusion from unaligned multimodal sequences. To this end, we propose the Progressive Modality Reinforcement (PMR) approach based on the recent advances of crossmodal transformer. Our approach introduces a message hub to exchange information with each modality. The message hub sends common messages to each modality and reinforces their features via crossmodal attention. In turn, it also collects the reinforced features from each modality and uses them to generate a reinforced common message. By repeating the cycle process, the common message and the modalities' features can progressively complement each other. Finally, the reinforced features are used to make predictions for human emotion. Comprehensive experiments on different human multimodal emotion recognition benchmarks clearly demonstrate the superiority of our approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.html | CVPR 2021 | null | null |
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in an Open World | Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe | In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a model with labeled data, and then identify new classes in unlabeled data via unsupervised clustering. However, the labeled data that provide essential knowledge are often underexplored in the second step. The challenge is that the labeled and unlabeled examples are from non-overlapping classes, which makes it difficult to build a learning relationship between them. In this work, we introduce OpenMix to mix the unlabeled examples from an open set and the labeled examples from known classes, where their non-overlapping labels and pseudo-labels are simultaneously mixed into a joint label distribution. OpenMix dynamically compounds examples in two ways. First, we produce mixed training images by incorporating labeled examples with unlabeled examples. With the benefit of unique prior knowledge in novel class discovery, the generated pseudo-labels will be more credible than the original unlabeled predictions. As a result, OpenMix helps preventing the model from overfitting on unlabeled samples that may be assigned with wrong pseudo-labels. Second, the first way encourages the unlabeled examples with high class-probabilities to have considerable accuracy. We introduce these examples as reliable anchors and further integrate them with unlabeled samples. This enables us to generate more combinations in unlabeled examples and exploit finer object relations among the new classes. Experiments on three classification datasets demonstrate the effectiveness of the proposed OpenMix, which is superior to state-of-the-art methods in novel class discovery. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.05551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.html | CVPR 2021 | null | null |
Combining Semantic Guidance and Deep Reinforcement Learning for Generating Human Level Paintings | Jaskirat Singh, Liang Zheng | Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds and Stanford Cars-196 datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI dataset. Source code and models are available at https://github.com/1jsingh/semantic-guidance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12589 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_Combining_Semantic_Guidance_CVPR_2021_supplemental.pdf | null |
Event-Based Bispectral Photometry Using Temporally Modulated Illumination | Tsuyoshi Takatani, Yuzuha Ito, Ayaka Ebisu, Yinqiang Zheng, Takahito Aoto | Analysis of bispectral difference plays a critical role in various applications that involve rays propagating in a light absorbing medium. In general, the bispectral difference is obtained by subtracting signals at two individual wavelengths captured by ordinary digital cameras, which tends to inherit the drawbacks of conventional cameras in dynamic range, response speed and quantization precision. In this paper, we propose a novel method to obtain a bispectral difference image using an event camera with temporally modulated illumination. Our method is rooted in a key observation on the analogy between the bispectral photometry principle of the participating medium and the event generating mechanism in an event camera. By carefully modulating the bispectral illumination, our method allows to read out the bispectral difference directly from triggered events. Experiments using a prototype imaging system have verified the feasibility of this novel usage of event cameras in photometry based vision tasks, such as 3D shape reconstruction in water. | https://openaccess.thecvf.com/content/CVPR2021/papers/Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Takatani_Event-Based_Bispectral_Photometry_CVPR_2021_supplemental.pdf | null |
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection | Jin Fang, Xinxin Zuo, Dingfu Zhou, Shengze Jin, Sen Wang, Liangjun Zhang | Annotating the LiDAR point cloud is crucial for deep learning-based 3D object detection tasks. Due to expensive labeling costs, data augmentation has been taken as a necessary module and plays an important role in training the neural network. "Copy" and "paste" (i.e., GT-Aug) is the most commonly used data augmentation strategy, however, the occlusion between objects has not been taken into consideration. To handle the above limitation, we propose a rendering-based LiDAR augmentation framework (i.e., LiDAR-Aug) to enrich the training data and boost the performance of LiDAR-based 3D object detectors. The proposed LiDAR-Aug is a plug-and-play module that can be easily integrated into different types of 3D object detection frameworks. Compared to the traditional object augmentation methods, LiDAR-Aug is more realistic and effective. Finally, we verify the proposed framework on the public KITTI dataset with different 3D object detectors. The experimental results show the superiority of our method compared to other data augmentation strategies. We plan to make our data and code public to help other researchers reproduce our results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_LiDAR-Aug_A_General_Rendering-Based_Augmentation_Framework_for_3D_Object_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_LiDAR-Aug_A_General_Rendering-Based_Augmentation_Framework_for_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_LiDAR-Aug_A_General_Rendering-Based_Augmentation_Framework_for_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Semantic-Aware Knowledge Distillation for Few-Shot Class-Incremental Learning | Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, Mehrtash Harandi | Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the limited number of examples for training, the techniques developed for standard incremental learning cannot be applied verbatim to FSCIL. In this work, we introduce a distillation algorithm to address the problem of FSCIL and propose to make use of semantic information during training. To this end, we make use of word embeddings as semantic information which is cheap to obtain and which facilitate the distillation process. Furthermore, we propose a method based on an attention mechanism on multiple parallel embeddings of visual data to align visual and semantic vectors, which reduces issues related to catastrophic forgetting. Via experiments on MiniImageNet, CUB200, and CIFAR100 dataset, we establish new state-of-the-art results by outperforming existing approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheraghian_Semantic-Aware_Knowledge_Distillation_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04059 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheraghian_Semantic-Aware_Knowledge_Distillation_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheraghian_Semantic-Aware_Knowledge_Distillation_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
General Instance Distillation for Object Detection | Xing Dai, Zeren Jiang, Zhao Wu, Yiping Bao, Zhicheng Wang, Si Liu, Erjin Zhou | In recent years, knowledge distillation has been proved to be an effective solution for model compression. This approach can make lightweight student models acquire the knowledge extracted from cumbersome teacher models. However, previous distillation methods of detection have weak generalization for different detection frameworks and rely heavily on ground truth (GT), ignoring the valuable relation information between instances. Thus, we propose a novel distillation method for detection tasks based on discriminative instances without considering the positive or negative distinguished by GT, which is called general instance distillation (GID). Our approach contains a general instance selection module (GISM) to make full use of feature-based, relation-based and response-based knowledge for distillation. Extensive results demonstrate that the student model achieves significant AP improvement and even outperforms the teacher in various detection frameworks. Specifically, RetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which surpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based teacher model with 38.1% AP. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification | Fengxiang Yang, Zhun Zhong, Zhiming Luo, Yuanzheng Cai, Yaojin Lin, Shaozi Li, Nicu Sebe | This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. Although this kind of approach has shown promising accuracy, it is hampered by 1) noisy labels produced by clustering and 2) feature variations caused by camera shift. The former will lead to incorrect optimization and thus hinders the model accuracy. The latter will result in assigning the intra-class samples of different cameras to different pseudo-label, making the model sensitive to camera variations. In this paper, we propose a unified framework to solve both problems. Concretely, we propose a Dynamic and Symmetric Cross-Entropy loss (DSCE) to deal with noisy samples and a camera-aware meta-learning algorithm (MetaCam) to adapt camera shift. DSCE can alleviate the negative effects of noisy samples and accommodate the change of clusters after each clustering step. MetaCam simulates cross-camera constraint by splitting the training data into meta-train and meta-test based on camera IDs. With the interacted gradient from meta-train and meta-test, the model is enforced to learn camera-invariant features. Extensive experiments on three re-ID benchmarks show the effectiveness and the complementary of the proposed DSCE and MetaCam. Our method outperforms the state-of-the-art methods on both fully unsupervised re-ID and unsupervised domain adaptive re-ID. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04618 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Joint_Noise-Tolerant_Learning_CVPR_2021_supplemental.pdf | null |
Mutual Graph Learning for Camouflaged Object Detection | Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, Deng-Ping Fan | Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an ideal model should be able to seek valuable, extra clues from the given scene and incorporate them into a joint learning framework for representation co-enhancement. With this inspiration, we design a novel Mutual Graph Learning (MGL) model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. Specifically, MGL decouples an image into two task-specific feature maps -- one for roughly locating the target and the other for accurately capturing its boundary details -- and fully exploits the mutual benefits by recurrently reasoning their high-order relations through graphs. Importantly, in contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations to maximize information interactions. Experiments on challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhai_Mutual_Graph_Learning_for_Camouflaged_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02613 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhai_Mutual_Graph_Learning_for_Camouflaged_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhai_Mutual_Graph_Learning_for_Camouflaged_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Single Pair Cross-Modality Super Resolution | Guy Shacht, Dov Danon, Sharon Fogel, Daniel Cohen-Or | Non-visual imaging sensors are widely used in the industry for different purposes. Those sensors are more expensive than visual (RGB) sensors, and usually produce images with lower resolution. To this end, Cross-Modality Super-Resolution methods were introduced, where an RGB image of a high-resolution assists in increasing the resolution of a low-resolution modality. However, fusing images from different modalities is not a trivial task, since each multi-modal pair varies greatly in its internal correlations. For this reason, traditional state-of-the-arts which are trained on external datasets often struggle with yielding an artifact-free result that is still loyal to the target modality characteristics. We present CMSR, a single-pair approach for Cross-Modality Super-Resolution. The network is internally trained on the two input images only, in a self-supervised manner, learns their internal statistics and correlations, and applies them to upsample the target modality. CMSR contains an internal transformer which is trained on-the-fly together with the up-sampling process itself and without supervision, to allow dealing with pairs that are only weakly aligned. We show that CMSR produces state-of-the-art super resolved images, yet without introducing artifacts or irrelevant details that originate from the RGB image only. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shacht_Single_Pair_Cross-Modality_Super_Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.09965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shacht_Single_Pair_Cross-Modality_Super_Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shacht_Single_Pair_Cross-Modality_Super_Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shacht_Single_Pair_Cross-Modality_CVPR_2021_supplemental.pdf | null |
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation | Tianfei Zhou, Jianwu Li, Xueyi Li, Ling Shao | This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, widely used in both stages, are not reliable for characterizing unseen objects, leading to poor generalization. To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation. In particular, to address instance discrimination, we propose to combine foreground region estimation and instance grouping together in one network, and additionally introduce temporal guidance for segmenting each frame, enabling more accurate object discovery. For temporal association, we complement current video object segmentation architectures with a discriminative appearance model, capable of capturing more fine-grained target-specific information. Given object proposals from the instance discrimination network, three essential strategies are adopted to achieve accurate segmentation: 1) target-specific tracking using a memory-augmented appearance model; 2) target-agnostic verification to trace possible tracklets for the proposal; 3) adaptive memory updating using the verified segments. We evaluate the proposed approach on DAVIS_ 17 and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Target-Aware_Object_Discovery_and_Association_for_Unsupervised_Video_Multi-Object_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04782 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Target-Aware_Object_Discovery_and_Association_for_Unsupervised_Video_Multi-Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Target-Aware_Object_Discovery_and_Association_for_Unsupervised_Video_Multi-Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Cross-View Regularization for Domain Adaptive Panoptic Segmentation | Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu | Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. On the other hand, most existing research was conducted under a supervised learning setup whereas domain adaptive panoptic segmentation which is critical in different tasks and applications is largely neglected. We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization for optimal domain adaptive panoptic segmentation. The inter-style consistency leverages geometric invariance across the same image of the different styles which ` fabricates' certain self-supervisions to guide the network to learn domain-invariant features. The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains. Extensive experiments over multiple domain adaptive panoptic segmentation tasks (e.g. synthetic-to-real and real-to-real) show that our proposed network achieves superior segmentation performance as compared with the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02584 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Cross-View_Regularization_for_CVPR_2021_supplemental.pdf | null |
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