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Active Learning by Feature Mixing | Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Gholamreza (Reza) Haffari, Anton van den Hengel, Javen Qinfeng Shi | The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is especially challenging with high-dimensional data (e. g. images, videos) and in low-data regimes. In this paper, we propose a novel method for batch AL called ALFA-Mix. We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations. We construct interpolations between representations of labelled and unlabelled instances then examine the predicted labels. We show that inconsistencies in these predictions help discovering features that the model is unable to recognise in the unlabelled instances. We derive an efficient implementation based on a closed-form solution to the optimal interpolation causing changes in predictions. Our method outperforms all recent AL approaches in 30 different settings on 12 benchmarks of images, videos, and non-visual data. The improvements are especially significant in low-data regimes and on self-trained vision transformers, where ALFA-Mix outperforms the state-of-the-art in 59% and 43% of the experiments respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Parvaneh_Active_Learning_by_Feature_Mixing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Parvaneh_Active_Learning_by_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parvaneh_Active_Learning_by_Feature_Mixing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parvaneh_Active_Learning_by_Feature_Mixing_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Accurate Facial Landmark Detection via Cascaded Transformers | Hui Li, Zidong Guo, Seon-Min Rhee, Seungju Han, Jae-Joon Han | Accurate facial landmarks are essential prerequisites for many tasks related to human faces. In this paper, an accurate facial landmark detector is proposed based on cascaded transformers. We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end. With self-attention in transformers, our model can inherently exploit the structured relationships between landmarks, which would benefit landmark detection under challenging conditions such as large pose and occlusion. During cascaded refinement, our model is able to extract the most relevant image features around the target landmark for coordinate prediction, based on deformable attention mechanism, thus bringing more accurate alignment. In addition, we propose a novel decoder that refines image features and landmark positions simultaneously. With few parameter increasing, the detection performance improves further. Our model achieves new state-of- the-art performance on several standard facial landmark detection benchmarks, and shows good generalization ability in cross-dataset evaluation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Towards_Accurate_Facial_Landmark_Detection_via_Cascaded_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Towards_Accurate_Facial_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_Accurate_Facial_Landmark_Detection_via_Cascaded_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_Accurate_Facial_Landmark_Detection_via_Cascaded_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Class-Aware Contrastive Semi-Supervised Learning | Fan Yang, Kai Wu, Shuyi Zhang, Guannan Jiang, Yong Liu, Feng Zheng, Wei Zhang, Chengjie Wang, Long Zeng | Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the noise contained in self-generated artificial labels. Moreover, the model's judgment becomes noisier in real-world applications with extensive out-of-distribution data. To address this issue, we propose a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL), which is a drop-in helper to improve the pseudo-label quality and enhance the model's robustness in the real-world setting. Rather than treating real-world data as a union set, our method separately handles reliable in-distribution data with class-wise clustering for blending into downstream tasks and noisy out-of-distribution data with image-wise contrastive for better generalization. Furthermore, by applying target re-weighting, we successfully emphasize clean label learning and simultaneously reduce noisy label learning. Despite its simplicity, our proposed CCSSL has significant performance improvements over the state-of-the-art SSL methods on the standard datasets CIFAR100 and STL10. On the real-world dataset Semi-iNat 2021, we improve FixMatch by 9.80% and CoMatch by 3.18%. Code is available https://github.com/TencentYoutuResearch/Classification-SemiCLS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Class-Aware_Contrastive_Semi-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Class-Aware_Contrastive_Semi-Supervised_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02261 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Class-Aware_Contrastive_Semi-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Class-Aware_Contrastive_Semi-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Long-Term Visual Map Sparsification With Heterogeneous GNN | Ming-Fang Chang, Yipu Zhao, Rajvi Shah, Jakob J. Engel, Michael Kaess, Simon Lucey | We address the problem of map sparsification for longterm visual localization. A commonly employed assumption in map sparsification is that the pre-build map and the later capture localization query are consistent. However, this assumption can be easily violated in the dynamic world. Additionally, the map size grows as new data accumulate through time, causing large data overhead in the long term. In this paper, we aim to overcome the environmental changes and reduce the map size at the same time by selecting points that are valuable to future localization. Inspired by the recent progress in Graph Neural Network (GNN), we propose the first work that models SfM maps as heterogeneous graphs and predicts 3D point importance scores with a GNN, which enables us to directly exploit the rich information in the SfM map graph. Two novel supervisions are proposed: 1) a data-fitting term for selecting valuable points to future localization based on training queries; 2) a K-Cover term for selecting sparse points with full-map coverage. In the experiments on a long-term dataset with environmental changes, our method selected map points on stable and widely visible structures and outperformed baselines in localization performance. This work novelly connects SfM maps with the abundant modern GNN techniques and opens a new research avenue forward. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_Long-Term_Visual_Map_Sparsification_With_Heterogeneous_GNN_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chang_Long-Term_Visual_Map_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15182 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_Long-Term_Visual_Map_Sparsification_With_Heterogeneous_GNN_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_Long-Term_Visual_Map_Sparsification_With_Heterogeneous_GNN_CVPR_2022_paper.html | CVPR 2022 | null |
Debiased Learning From Naturally Imbalanced Pseudo-Labels | Xudong Wang, Zhirong Wu, Long Lian, Stella X. Yu | This work studies the bias issue of pseudo-labeling, a natural phenomenon that widely occurs but often overlooked by prior research. Pseudo-labels are generated when a classifier trained on source data is transferred to unlabeled target data. We observe heavy long-tailed pseudo-labels when a semi-supervised learning model FixMatch predicts labels on the unlabeled set even though the unlabeled data is curated to be balanced. Without intervention, the training model inherits the bias from the pseudo-labels and end up being sub-optimal. To eliminate the model bias, we propose a simple yet effective method DebiasMatch, comprising of an adaptive debiasing module and an adaptive marginal loss. The strength of debiasing and the size of margins can be automatically adjusted by making use of an online updated queue. Benchmarked on ImageNet-1K, DebiasMatch significantly outperforms previous state-of-the-arts by more than 26% and 10.5% on semi-supervised learning (0.2% annotated data) and zero-shot learning tasks respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.01490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.html | CVPR 2022 | null |
RNNPose: Recurrent 6-DoF Object Pose Refinement With Robust Correspondence Field Estimation and Pose Optimization | Yan Xu, Kwan-Yee Lin, Guofeng Zhang, Xiaogang Wang, Hongsheng Li | 6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a non-linear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses. Furthermore, to improve the robustness to occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_RNNPose_Recurrent_6-DoF_Object_Pose_Refinement_With_Robust_Correspondence_Field_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_RNNPose_Recurrent_6-DoF_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_RNNPose_Recurrent_6-DoF_Object_Pose_Refinement_With_Robust_Correspondence_Field_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_RNNPose_Recurrent_6-DoF_Object_Pose_Refinement_With_Robust_Correspondence_Field_CVPR_2022_paper.html | CVPR 2022 | null |
Ditto: Building Digital Twins of Articulated Objects From Interaction | Zhenyu Jiang, Cheng-Chun Hsu, Yuke Zhu | Digitizing physical objects into the virtual world has the potential to unlock new research and applications in embodied AI and mixed reality. This work focuses on recreating interactive digital twins of real-world articulated objects, which can be directly imported into virtual environments. We introduce Ditto to learn articulation model estimation and 3D geometry reconstruction of an articulated object through interactive perception. Given a pair of visual observations of an articulated object before and after interaction, Ditto reconstructs part-level geometry and estimates the articulation model of the object. We employ implicit neural representations for joint geometry and articulation modeling. Our experiments show that Ditto effectively builds digital twins of articulated objects in a category-agnostic way. We also apply Ditto to real-world objects and deploy the recreated digital twins in physical simulation. Code and additional results are available at https://ut-austin-rpl.github.io/Ditto/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Ditto_Building_Digital_Twins_of_Articulated_Objects_From_Interaction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Ditto_Building_Digital_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.08227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Ditto_Building_Digital_Twins_of_Articulated_Objects_From_Interaction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Ditto_Building_Digital_Twins_of_Articulated_Objects_From_Interaction_CVPR_2022_paper.html | CVPR 2022 | null |
Dual-AI: Dual-Path Actor Interaction Learning for Group Activity Recognition | Mingfei Han, David Junhao Zhang, Yali Wang, Rui Yan, Lina Yao, Xiaojun Chang, Yu Qiao | Learning spatial-temporal relation among multiple actors is crucial for group activity recognition. Different group activities often show the diversified interactions between actors in the video. Hence, it is often difficult to model complex group activities from a single view of spatial-temporal actor evolution. To tackle this problem, we propose a distinct Dual-path Actor Interaction (Dual-AI) framework, which flexibly arranges spatial and temporal transformers in two complementary orders, enhancing actor relations by integrating merits from different spatio-temporal paths. Moreover, we introduce a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive paths of Dual-AI. Via self-supervised actor consistency in both frame and video levels, MAC-Loss can effectively distinguish individual actor representations to reduce action confusion among different actors. Consequently, our Dual-AI can boost group activity recognition by fusing such discriminative features of different actors. To evaluate the proposed approach, we conduct extensive experiments on the widely used benchmarks, including Volleyball, Collective Activity, and NBA datasets. The proposed Dual-AI achieves state-of-the-art performance on all these datasets. It is worth noting the proposed Dual-AI with 50% training data outperforms a number of recent approaches with 100% training data. This confirms the generalization power of Dual-AI for group activity recognition, even under the challenging scenarios of limited supervision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Dual-AI_Dual-Path_Actor_Interaction_Learning_for_Group_Activity_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Dual-AI_Dual-Path_Actor_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Dual-AI_Dual-Path_Actor_Interaction_Learning_for_Group_Activity_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Dual-AI_Dual-Path_Actor_Interaction_Learning_for_Group_Activity_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations | Mostofa Rafid Uddin, Gregory Howe, Xiangrui Zeng, Min Xu | In many real-life image analysis applications, particularly in biomedical research domains, the objects of interest undergo multiple transformations that alters their visual properties while keeping the semantic content unchanged. Disentangling images into semantic content factors and transformations can provide significant benefits into many domain-specific image analysis tasks. To this end, we propose a generic unsupervised framework, Harmony, that simultaneously and explicitly disentangles semantic content from multiple parameterized transformations. Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations. To demonstrate the efficacy of Harmony, we apply it to disentangle image semantic content from several parameterized transformations (rotation, translation, scaling, and contrast). Harmony achieves significantly improved disentanglement over the baseline models on several image datasets of diverse domains. With such disentanglement, Harmony is demonstrated to incentivize bioimage analysis research by modeling structural heterogeneity of macromolecules from cryo-ET images and learning transformation-invariant representations of protein particles from single-particle cryo-EM images. Harmony also performs very well in disentangling content from 3D transformations and can perform coarse and fast alignment of 3D cryo-ET subtomograms. Therefore, Harmony is generalizable to many other imaging domains and can potentially be extended to domains beyond imaging as well. | https://openaccess.thecvf.com/content/CVPR2022/papers/Uddin_Harmony_A_Generic_Unsupervised_Approach_for_Disentangling_Semantic_Content_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Uddin_Harmony_A_Generic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Uddin_Harmony_A_Generic_Unsupervised_Approach_for_Disentangling_Semantic_Content_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Uddin_Harmony_A_Generic_Unsupervised_Approach_for_Disentangling_Semantic_Content_From_CVPR_2022_paper.html | CVPR 2022 | null |
Talking Face Generation With Multilingual TTS | Hyoung-Kyu Song, Sang Hoon Woo, Junhyeok Lee, Seungmin Yang, Hyunjae Cho, Youseong Lee, Dongho Choi, Kang-wook Kim | Recent studies in talking face generation have focused on building a model that can generalize from any source speech to any target identity. A number of works have already claimed this functionality and have added that their models will also generalize to any language. However, we show, using languages from different language families, that these models do not translate well when the training language and the testing language are sufficiently different. We reduce the scope of the problem to building a languagerobust talking face generation system on seen identities, i.e., the target identity is the same as the training identity. In this work, we introduce a talking face generation system that generalizes to different languages. We evaluate the efficacy of our system using a multilingual text-to-speech system. We present the joint text-to-speech system and the talking face generation system as a neural dubber system. Our demo is available at https://bit.ly/ml-face-generation-cvpr22-demo. Also, our screencast is uploaded at https://youtu.be/F6h0s0M4vBI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.06421 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.html | CVPR 2022 | null |
A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres | Jinwoo Kim, Heeseok Oh, Seongjean Kim, Hoseok Tong, Sanghoon Lee | When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we propose MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learn a dance-genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. We conduct extensive experiments on AIST++ along with user studies. Compared to the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates outperforming dance sequences qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_A_Brand_New_Dance_Partner_Music-Conditioned_Pluralistic_Dancing_Controlled_by_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_A_Brand_New_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Brand_New_Dance_Partner_Music-Conditioned_Pluralistic_Dancing_Controlled_by_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Brand_New_Dance_Partner_Music-Conditioned_Pluralistic_Dancing_Controlled_by_CVPR_2022_paper.html | CVPR 2022 | null |
Kernelized Few-Shot Object Detection With Efficient Integral Aggregation | Shan Zhang, Lei Wang, Naila Murray, Piotr Koniusz | We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly. Our pipeline contains several modules. An Encoding Network encodes support and query images. Our Kernelized Autocorrelation unit forms the linear, polynomial and RBF kernelized representations from features extracted within support regions of support images. These features are then cross-correlated against features of a query image to obtain attention weights, and generate query proposal regions via an Attention Region Proposal Net. As the query proposal regions are many, each described by the linear, polynomial and RBF kernelized matrices, their formation is costly but that cost is reduced by our proposed Integral Region-of-Interest Aggregation unit. Finally, the Multi-head Relation Net combines all kernelized (second-order) representations with the first-order feature maps to learn support-query class relations and locations. We outperform the state of the art on novel classes by 3.8%, 5.4% and 5.7% mAP on PASCAL VOC 2007, FSOD, and COCO. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Kernelized_Few-Shot_Object_Detection_With_Efficient_Integral_Aggregation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Kernelized_Few-Shot_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Kernelized_Few-Shot_Object_Detection_With_Efficient_Integral_Aggregation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Kernelized_Few-Shot_Object_Detection_With_Efficient_Integral_Aggregation_CVPR_2022_paper.html | CVPR 2022 | null |
Transformer Based Line Segment Classifier With Image Context for Real-Time Vanishing Point Detection in Manhattan World | Xin Tong, Xianghua Ying, Yongjie Shi, Ruibin Wang, Jinfa Yang | Previous works on vanishing point detection usually use geometric prior for line segment clustering. We find that image context can also contribute to accurate line classification. Based on this observation, we propose to classify line segments into three groups according to three unknown-but-sought vanishing points with Manhattan world assumption, using both geometric information and image context in this work. To achieve this goal, we propose a novel Transformer based Line segment Classifier (TLC) that can group line segments in images and estimate the corresponding vanishing points. In TLC, we design a line segment descriptor to represent line segments using their positions, directions and local image contexts. Transformer based feature fusion module is used to capture global features from all line segments, which is proved to improve the classification performance significantly in our experiments. By using a network to score line segments for outlier rejection, vanishing points can be got by Singular Value Decomposition (SVD) from the classified lines. The proposed method runs at 25 fps on one NVIDIA 2080Ti card for vanishing point detection. Experimental results on synthetic and real-world datasets demonstrate that our method is superior to other state-of-the-art methods on the balance between accuracy and efficiency, while keeping stronger generalization capability when trained and evaluated on different datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tong_Transformer_Based_Line_Segment_Classifier_With_Image_Context_for_Real-Time_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tong_Transformer_Based_Line_Segment_Classifier_With_Image_Context_for_Real-Time_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tong_Transformer_Based_Line_Segment_Classifier_With_Image_Context_for_Real-Time_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning | Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, Zheng-Jun Zha | Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be achieved under supervision from new classes. To address this problem, we propose a novel self-sustaining representation expansion scheme. Our scheme consists of a structure reorganization strategy that fuses main-branch expansion and side-branch updating to maintain the old features, and a main-branch distillation scheme to transfer the invariant knowledge. Furthermore, a prototype selection mechanism is proposed to enhance the discrimination between the old and new classes by selectively incorporating new samples into the distillation process. Extensive experiments on three benchmarks demonstrate significant incremental performance, outperforming the state-of-the-art methods by a margin of 3% , 3% and 6% , respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Self-Sustaining_Representation_Expansion_for_Non-Exemplar_Class-Incremental_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Self-Sustaining_Representation_Expansion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Self-Sustaining_Representation_Expansion_for_Non-Exemplar_Class-Incremental_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Self-Sustaining_Representation_Expansion_for_Non-Exemplar_Class-Incremental_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Adaptive Early-Learning Correction for Segmentation From Noisy Annotations | Sheng Liu, Kangning Liu, Weicheng Zhu, Yiqiu Shen, Carlos Fernandez-Granda | Deep learning in the presence of noisy annotations has been studied extensively in classification, but much less in segmentation tasks. In this work, we study the learning dynamics of deep segmentation networks trained on inaccurately-annotated data. We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations. However, in contrast to classification, memorization in segmentation does not arise simultaneously for all semantic categories. Inspired by these findings, we propose a new method for segmentation from noisy annotations with two key elements. First, we detect the beginning of the memorization phase separately for each category during training. This allows us to adaptively correct the noisy annotations in order to exploit early learning. Second, we incorporate a regularization term that enforces consistency across scales to boost robustness against annotation noise. Our method outperforms standard approaches on a medical-imaging segmentation task where noises are synthesized to mimic human annotation errors. It also provides robustness to realistic noisy annotations present in weakly-supervised semantic segmentation, achieving state-of-the-art results on PASCAL VOC 2012. Code is available at https://github.com/Kangningthu/ADELE | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Adaptive_Early-Learning_Correction_for_Segmentation_From_Noisy_Annotations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Adaptive_Early-Learning_Correction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.03740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Adaptive_Early-Learning_Correction_for_Segmentation_From_Noisy_Annotations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Adaptive_Early-Learning_Correction_for_Segmentation_From_Noisy_Annotations_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation | Huan Gao, Jichang Guo, Guoli Wang, Qian Zhang | The performance of nighttime semantic segmentation is restricted by the poor illumination and a lack of pixel-wise annotation, which severely limit its application in autonomous driving. Existing works, e.g., using the twilight as the intermediate target domain to perform the adaptation from daytime to nighttime, may fail to cope with the inherent difference between datasets caused by the camera equipment and the urban style. Faced with these two types of domain shifts, i.e., the illumination and the inherent difference of the datasets, we propose a novel domain adaptation framework via cross-domain correlation distillation, called CCDistill. The invariance of illumination or inherent difference between two images is fully explored so as to make up for the lack of labels for nighttime images. Specifically, we extract the content and style knowledge contained in features, calculate the degree of inherent or illumination difference between two images. The domain adaptation is achieved using the invariance of the same kind of difference. Extensive experiments on Dark Zurich and ACDC demonstrate that CCDistill achieves the state-of-the-art performance for nighttime semantic segmentation. Notably, our method is a one-stage domain adaptation network which can avoid affecting the inference time. Our implementation is available at https://github.com/ghuan99/CCDistill. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_Cross-Domain_Correlation_Distillation_for_Unsupervised_Domain_Adaptation_in_Nighttime_Semantic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gao_Cross-Domain_Correlation_Distillation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.00858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Cross-Domain_Correlation_Distillation_for_Unsupervised_Domain_Adaptation_in_Nighttime_Semantic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Cross-Domain_Correlation_Distillation_for_Unsupervised_Domain_Adaptation_in_Nighttime_Semantic_CVPR_2022_paper.html | CVPR 2022 | null |
Context-Aware Video Reconstruction for Rolling Shutter Cameras | Bin Fan, Yuchao Dai, Zhiyuan Zhang, Qi Liu, Mingyi He | With the ubiquity of rolling shutter (RS) cameras, it is becoming increasingly attractive to recover the latent global shutter (GS) video from two consecutive RS frames, which also places a higher demand on realism. Existing solutions, using deep neural networks or optimization, achieve promising performance. However, these methods generate intermediate GS frames through image warping based on the RS model, which inevitably result in black holes and noticeable motion artifacts. In this paper, we alleviate these issues by proposing a context-aware GS video reconstruction architecture. It facilitates the advantages such as occlusion reasoning, motion compensation, and temporal abstraction. Specifically, we first estimate the bilateral motion field so that the pixels of the two RS frames are warped to a common GS frame accordingly. Then, a refinement scheme is proposed to guide the GS frame synthesis along with bilateral occlusion masks to produce high-fidelity GS video frames at arbitrary times. Furthermore, we derive an approximated bilateral motion field model, which can serve as an alternative to provide a simple but effective GS frame initialization for related tasks. Experiments on synthetic and real data show that our approach achieves superior performance over state-of-the-art methods in terms of objective metrics and subjective visual quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_Context-Aware_Video_Reconstruction_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.12912 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Efficient Data Free Black-Box Adversarial Attack | Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, Chao Wu | Classic black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model. However, these substitute models need to be trained by target models' training data, which is hard to acquire due to privacy or transmission reasons. Recognizing the limited availability of real data for adversarial queries, recent works proposed to train substitute models in a data-free black-box scenario. However, their generative adversarial networks (GANs) based framework suffers from the convergence failure and the model collapse, resulting in low efficiency. In this paper, by rethinking the collaborative relationship between the generator and the substitute model, we design a novel black-box attack framework. The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate. The comprehensive experiments over six datasets demonstrate the effectiveness of our method against the state-of-the-art attacks. Especially, we conduct both label-only and probability-only attacks on the Microsoft Azure online model, and achieve a 100% attack success rate with only 0.46% query budget of the SOTA method [??]. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Towards_Efficient_Data_Free_Black-Box_Adversarial_Attack_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Towards_Efficient_Data_Free_Black-Box_Adversarial_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Towards_Efficient_Data_Free_Black-Box_Adversarial_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Contrastive Learning Against Noisy Views | Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song | Contrastive learning relies on an assumption that positive pairs contain related views that share certain underlying information about an instance, e.g., patches of an image or co-occurring multimodal signals of a video. What if this assumption is violated? The literature suggests that contrastive learning produces suboptimal representations in the presence of noisy views, e.g., false positive pairs with no apparent shared information. In this work, we propose a new contrastive loss function that is robust against noisy views. We provide rigorous theoretical justifications by showing connections to robust symmetric losses for noisy binary classification and by establishing a new contrastive bound for mutual information maximization based on the Wasserstein distance measure. The proposed loss is completely modality-agnostic and a simple drop-in replacement for the InfoNCE loss, which makes it easy to apply to existing contrastive frameworks. We show that our approach provides consistent improvements over the state-of-the-art on image, video, and graph contrastive learning benchmarks that exhibit a variety of real-world noise patterns. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chuang_Robust_Contrastive_Learning_Against_Noisy_Views_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chuang_Robust_Contrastive_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04309 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chuang_Robust_Contrastive_Learning_Against_Noisy_Views_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chuang_Robust_Contrastive_Learning_Against_Noisy_Views_CVPR_2022_paper.html | CVPR 2022 | null |
More Than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech | Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford, Miaosen Wang, Ye Jia, Tal Remez | In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes advantage of video frames as an additional input alongside text, and generates speech that matches the video signal. We demonstrate how this allows VDTTS to, unlike plain TTS models, generate speech that not only has prosodic variations like natural pauses and pitch, but is also synchronized to the input video. Experimentally, we show our model produces well-synchronized outputs, approaching the video-speech synchronization quality of the ground-truth, on several challenging benchmarks including "in-the-wild" content from VoxCeleb2. Supplementary demo videos demonstrating video-speech synchronization, robustness to speaker ID swapping, and prosody, presented at the project page. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hassid_More_Than_Words_In-the-Wild_Visually-Driven_Prosody_for_Text-to-Speech_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hassid_More_Than_Words_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.10139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hassid_More_Than_Words_In-the-Wild_Visually-Driven_Prosody_for_Text-to-Speech_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hassid_More_Than_Words_In-the-Wild_Visually-Driven_Prosody_for_Text-to-Speech_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Modal Perceptionist: Can Face Geometry Be Gleaned From Voices? | Cho-Ying Wu, Chin-Cheng Hsu, Ulrich Neumann | This work digs into a root question in human perception: can face geometry be gleaned from one's voices? Previous works that study this question only adopt developments in image synthesis and convert voices into face images to show correlations, but working on the image domain unavoidably involves predicting attributes that voices cannot hint, including facial textures, hairstyles, and backgrounds. We instead investigate the ability to reconstruct 3D faces to concentrate on only geometry, which is much more physiologically grounded. We propose our analysis framework, Cross-Modal Perceptionist, under both supervised and unsupervised learning. First, we construct a dataset, Voxceleb-3D, which extends Voxceleb and includes paired voices and face meshes, making supervised learning possible. Second, we use a knowledge distillation mechanism to study whether face geometry can still be gleaned from voices without paired voices and 3D face data under limited availability of 3D face scans. We break down the core question into four parts and perform visual and numerical analyses as responses to the core question. Our findings echo those in physiology and neuroscience about the correlation between voices and facial structures. The work provides future human-centric cross-modal learning with explainable foundations. See our project page. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Cross-Modal_Perceptionist_Can_Face_Geometry_Be_Gleaned_From_Voices_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Cross-Modal_Perceptionist_Can_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09824 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Cross-Modal_Perceptionist_Can_Face_Geometry_Be_Gleaned_From_Voices_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Cross-Modal_Perceptionist_Can_Face_Geometry_Be_Gleaned_From_Voices_CVPR_2022_paper.html | CVPR 2022 | null |
On Generalizing Beyond Domains in Cross-Domain Continual Learning | Christian Simon, Masoud Faraki, Yi-Hsuan Tsai, Xiang Yu, Samuel Schulter, Yumin Suh, Mehrtash Harandi, Manmohan Chandraker | In the real world, humans have the ability to accumulate new knowledge in any conditions. However, deeplearning suffers from the phenomenon so-called catastrophic forgetting of the previously observed knowledge after learning a new task. Many recent methods focus on preventing catastrophic forgetting under a typical assumption of thetrain and test data following a similar distribution. In thiswork, we consider the more realistic scenario of continuallearning under domain shifts where the model is able to gen-eralize its inference to a an unseen domain. To this end, wepropose to make use of sample correlations of the learning tasks in the classifiers where the subsequent optimization isperformed over similarity measures obtained in a similar fashion to the Mahalanobis distance computation. In addition, we also propose an approach based on the exponential moving average of the parameters for better knowledge distillation, allowing a further adaptation to the old model. We demonstrate in our experiments that, to a great extent, the past continual learning algorithms fail to handle the forgetting issue under multiple distributions, while our proposed approach identifies the task under domain shift where insome cases can boost up the performance up to 10% on the challenging datasets e.g., DomainNet and OfficeHome. | https://openaccess.thecvf.com/content/CVPR2022/papers/Simon_On_Generalizing_Beyond_Domains_in_Cross-Domain_Continual_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Simon_On_Generalizing_Beyond_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03970 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Simon_On_Generalizing_Beyond_Domains_in_Cross-Domain_Continual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Simon_On_Generalizing_Beyond_Domains_in_Cross-Domain_Continual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
RSTT: Real-Time Spatial Temporal Transformer for Space-Time Video Super-Resolution | Zhicheng Geng, Luming Liang, Tianyu Ding, Ilya Zharkov | Space-time video super-resolution (STVSR) is the task of interpolating videos with both Low Frame Rate (LFR) and Low Resolution (LR) to produce High-Frame-Rate (HFR) and also High-Resolution (HR) counterparts. The existing methods based on Convolutional Neural Network (CNN) succeed in achieving visually satisfied results while suffer from slow inference speed due to their heavy architectures. We propose to resolve this issue by using a spatial-temporal transformer that naturally incorporates the spatial and temporal super resolution modules into a single model. Unlike CNN-based methods, we do not explicitly use separated building blocks for temporal interpolations and spatial super-resolutions; instead, we only use a single end-to-end transformer architecture. Specifically, a reusable dictionary is built by encoders based on the input LFR and LR frames, which is then utilized in the decoder part to synthesize the HFR and HR frames. Compared with the state-of-the-art TMNet, our network is 60% smaller (4.5M vs 12.3M parameters) and 80% faster (26.2fps vs 14.3fps on 720 x 576 frames) without sacrificing much performance. The source code is available at https://github.com/llmpass/RSTT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Geng_RSTT_Real-Time_Spatial_Temporal_Transformer_for_Space-Time_Video_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Geng_RSTT_Real-Time_Spatial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14186 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_RSTT_Real-Time_Spatial_Temporal_Transformer_for_Space-Time_Video_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_RSTT_Real-Time_Spatial_Temporal_Transformer_for_Space-Time_Video_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Memory-Augmented Unidirectional Metrics for Cross-Modality Person Re-Identification | Jialun Liu, Yifan Sun, Feng Zhu, Hongbin Pei, Yi Yang, Wenhui Li | This paper tackles the cross-modality person re-identification (re-ID) problem by suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images are in different modalities. Given a training identity, the popular deep classification baseline shares the same proxy (i.e., a weight vector in the last classification layer) for two modalities. We find that it has considerable tolerance for the modality gap, because the shared proxy acts as an intermediate relay between two modalities. In response, we propose a Memory-Augmented Unidirectional Metric (MAUM) learning method consisting of two novel designs, i.e., unidirectional metrics, and memory-based augmentation. Specifically, MAUM first learns modality-specific proxies (MS-Proxies) independently under each modality. Afterward, MAUM uses the already-learned MS-Proxies as the static references for pulling close the features in the counterpart modality. These two unidirectional metrics (IR image to RGB proxy and RGB image to IR proxy) jointly alleviate the relay effect and benefit cross-modality association. The cross-modality association is further enhanced by storing the MS-Proxies into memory banks to increase the reference diversity. Importantly, we show that MAUM improves cross-modality re-ID under the modality-balanced setting and gains extra robustness against the modality-imbalance problem. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate the superiority of MAUM over the state-of-the-art. The code will be available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_Memory-Augmented_Unidirectional_Metrics_for_Cross-Modality_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_Memory-Augmented_Unidirectional_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Memory-Augmented_Unidirectional_Metrics_for_Cross-Modality_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Memory-Augmented_Unidirectional_Metrics_for_Cross-Modality_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
A Closer Look at Few-Shot Image Generation | Yunqing Zhao, Henghui Ding, Houjing Huang, Ngai-Man Cheung | Modern GANs excel at generating high-quality and diverse images. However, when transferring the pretrained GANs on small target data (e.g., 10-shot), the generator tends to replicate the training samples. Several methods have been proposed to address this few-shot image generation task, but there is a lack of effort to analyze them under a unified framework. As our first contribution, we propose a framework to analyze existing methods during the adaptation. Our analysis discovers that while some methods have a disproportionate focus on diversity preserving which impedes quality improvement, all methods achieve similar quality after convergence. Therefore, the better methods are those that can slow down diversity degradation. Furthermore, our analysis reveals that there is still plenty of room to further slow down diversity degradation. Informed by our analysis and to slow down diversity degradation of the target generator during adaptation, our second contribution proposes to apply mutual information (MI) maximization to retain the source domain's rich multi-level diversity information in the target domain generator. We propose to perform MI maximization by contrastive loss (CL), leverage the generator and discriminator as two feature encoders to extract different multi-level features for computing CL. We refer to our method as Dual ContrastiveLearning (DCL). Extensive experiments on several public datasets show that, while leading to a slower diversity-degrading generator during adaptation, our proposed DCL brings visually pleasant quality and state-of-the-art quantitative performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_A_Closer_Look_at_Few-Shot_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_A_Closer_Look_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03805 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_A_Closer_Look_at_Few-Shot_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_A_Closer_Look_at_Few-Shot_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Depth-Supervised NeRF: Fewer Views and Faster Training for Free | Kangle Deng, Andrew Liu, Jun-Yan Zhu, Deva Ramanan | A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We formalize the above assumption through DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision. We leverage the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can render better images given fewer training views while training 2-3x faster. Further, we show that our loss is compatible with other recently proposed NeRF methods, demonstrating that depth is a cheap and easily digestible supervisory signal. And finally, we find that DS-NeRF can support other types of depth supervision such as scanned depth sensors and RGBD reconstruction outputs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2107.02791 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Domain Generalization by Learning a Bridge Across Domains | Sivan Harary, Eli Schwartz, Assaf Arbelle, Peter Staar, Shady Abu-Hussein, Elad Amrani, Roei Herzig, Amit Alfassy, Raja Giryes, Hilde Kuehne, Dina Katabi, Kate Saenko, Rogerio S. Feris, Leonid Karlinsky | The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system. In this paper, different from most cross-domain works that utilize some (or full) source domain supervision, we approach a relatively new and very practical Unsupervised Domain Generalization (UDG) setup of having no training supervision in neither source nor target domains. Our approach is based on self-supervised learning of a Bridge Across Domains (BrAD) - an auxiliary bridge domain accompanied by a set of semantics preserving visual (image-to-image) mappings to BrAD from each of the training domains. The BrAD and mappings to it are learned jointly (end-to-end) with a contrastive self-supervised representation model that semantically aligns each of the domains to its BrAD-projection, and hence implicitly drives all the domains (seen or unseen) to semantically align to each other. In this work, we show how using an edge-regularized BrAD our approach achieves significant gains across multiple benchmarks and a range of tasks, including UDG, Few-shot UDA, and unsupervised generalization across multi-domain datasets (including generalization to unseen domains and classes). | https://openaccess.thecvf.com/content/CVPR2022/papers/Harary_Unsupervised_Domain_Generalization_by_Learning_a_Bridge_Across_Domains_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Harary_Unsupervised_Domain_Generalization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Harary_Unsupervised_Domain_Generalization_by_Learning_a_Bridge_Across_Domains_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Harary_Unsupervised_Domain_Generalization_by_Learning_a_Bridge_Across_Domains_CVPR_2022_paper.html | CVPR 2022 | null |
Partial Class Activation Attention for Semantic Segmentation | Sun-Ao Liu, Hongtao Xie, Hai Xu, Yongdong Zhang, Qi Tian | Current attention-based methods for semantic segmentation mainly model pixel relation through pairwise affinity and coarse segmentation. For the first time, this paper explores modeling pixel relation via Class Activation Map (CAM). Beyond the previous CAM generated from image-level classification, we present Partial CAM, which subdivides the task into region-level prediction and achieves better localization performance. In order to eliminate the intra-class inconsistency caused by the variances of local context, we further propose Partial Class Activation Attention (PCAA) that simultaneously utilizes local and global class-level representations for attention calculation. Once obtained the partial CAM, PCAA collects local class centers and computes pixel-to-class relation locally. Applying local-specific representations ensures reliable results under different local contexts. To guarantee global consistency, we gather global representations from all local class centers and conduct feature aggregation. Experimental results confirm that Partial CAM outperforms the previous two strategies as pixel relation. Notably, our method achieves state-of-the-art performance on several challenging benchmarks including Cityscapes, Pascal Context, and ADE20K. Code is available at https://github.com/lsa1997/PCAA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Partial_Class_Activation_Attention_for_Semantic_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Partial_Class_Activation_Attention_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Partial_Class_Activation_Attention_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Scale Memory-Based Video Deblurring | Bo Ji, Angela Yao | Video deblurring has achieved remarkable progress thanks to the success of deep neural networks. Most methods solve for the deblurring end-to-end with limited information propagation from the video sequence. However, different frame regions exhibit different characteristics and should be provided with corresponding relevant information. To achieve fine-grained deblurring, we designed a memory branch to memorize the blurry-sharp feature pairs in the memory bank, thus providing useful information for the blurry query input. To enrich the memory of our memory bank, we further designed a bidirectional recurrency and multi-scale strategy based on the memory bank. Experimental results demonstrate that our model outperforms other state-of-the-art methods while keeping the model complexity and inference time low. The code is available at https://github.com/jibo27/MemDeblur. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ji_Multi-Scale_Memory-Based_Video_Deblurring_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ji_Multi-Scale_Memory-Based_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02977 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ji_Multi-Scale_Memory-Based_Video_Deblurring_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ji_Multi-Scale_Memory-Based_Video_Deblurring_CVPR_2022_paper.html | CVPR 2022 | null |
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters | Albert Mosella-Montoro, Javier Ruiz-Hidalgo | This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to generalize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mosella-Montoro_SkinningNet_Two-Stream_Graph_Convolutional_Neural_Network_for_Skinning_Prediction_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mosella-Montoro_SkinningNet_Two-Stream_Graph_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mosella-Montoro_SkinningNet_Two-Stream_Graph_Convolutional_Neural_Network_for_Skinning_Prediction_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mosella-Montoro_SkinningNet_Two-Stream_Graph_Convolutional_Neural_Network_for_Skinning_Prediction_of_CVPR_2022_paper.html | CVPR 2022 | null |
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching | Paul Roetzer, Paul Swoboda, Daniel Cremers, Florian Bernard | We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant formalism proposed by Windheuser et al. (ICCV, 2011) where 3D shape matching was formulated as an integer linear program over the space of orientation-preserving diffeomorphisms. Until now, the resulting formulation had limited practical applicability due to its complicated constraint structure and its large size. We propose a novel primal heuristic coupled with a Lagrange dual problem that is several orders of magnitudes faster compared to previous solvers. This allows us to handle shapes with substantially more triangles than previously solvable. We demonstrate compelling results on diverse datasets, and, even showcase that we can address the challenging setting of matching two partial shapes without availability of complete shapes. Our code is publicly available at http://github.com/paul0noah/sm-comb. | https://openaccess.thecvf.com/content/CVPR2022/papers/Roetzer_A_Scalable_Combinatorial_Solver_for_Elastic_Geometrically_Consistent_3D_Shape_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Roetzer_A_Scalable_Combinatorial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12805 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Roetzer_A_Scalable_Combinatorial_Solver_for_Elastic_Geometrically_Consistent_3D_Shape_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Roetzer_A_Scalable_Combinatorial_Solver_for_Elastic_Geometrically_Consistent_3D_Shape_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Trajectory-Aware Transformer for Video Super-Resolution | Chengxu Liu, Huan Yang, Jianlong Fu, Xueming Qian | Video super-resolution (VSR) aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Although some progress has been made, there are grand challenges to effectively utilize temporal dependency in entire video sequences. Existing approaches usually align and aggregate video frames from limited adjacent frames (e.g., 5 or 7 frames), which prevents these approaches from satisfactory results. In this paper, we take one step further to enable effective spatio-temporal learning in videos. We propose a novel Trajectory-aware Transformer for Video Super-Resolution (TTVSR). In particular, we formulate video frames into several pre-aligned trajectories which consist of continuous visual tokens. For a query token, self-attention is only learned on relevant visual tokens along spatio-temporal trajectories. Compared with vanilla vision Transformers, such a design significantly reduces the computational cost and enables Transformers to model long-range features. We further propose a cross-scale feature tokenization module to overcome scale-changing problems that often occur in long-range videos. Experimental results demonstrate the superiority of the proposed TTVSR over state-of-the-art models, by extensive quantitative and qualitative evaluations in four widely-used video super-resolution benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_Trajectory-Aware_Transformer_for_Video_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_Trajectory-Aware_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Trajectory-Aware_Transformer_for_Video_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Trajectory-Aware_Transformer_for_Video_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Differentiable Dynamics for Articulated 3D Human Motion Reconstruction | Erik Gärtner, Mykhaylo Andriluka, Erwin Coumans, Cristian Sminchisescu | We introduce DiffPhy, a differentiable physics-based model for articulated 3d human motion reconstruction from video. Applications of physics-based reasoning in human motion analysis have so far been limited, both by the complexity of constructing adequate physical models of articulated human motion, and by the formidable challenges of performing stable and efficient inference with physics in the loop. We jointly address such modeling and inference challenges by proposing an approach that combines a physically plausible body representation with anatomical joint limits, a differentiable physics simulator, and optimization techniques that ensure good performance and robustness to suboptimal local optima. In contrast to several recent methods, our approach readily supports full-body contact including interactions with objects in the scene. Most importantly, our model connects end-to-end with images, thus supporting direct gradient-based physics optimization by means of image-based loss functions. We validate the model by demonstrating that it can accurately reconstruct physically plausible 3d human motion from monocular video, both on public benchmarks with available 3d ground-truth, and on videos from the internet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gartner_Differentiable_Dynamics_for_Articulated_3D_Human_Motion_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gartner_Differentiable_Dynamics_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gartner_Differentiable_Dynamics_for_Articulated_3D_Human_Motion_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gartner_Differentiable_Dynamics_for_Articulated_3D_Human_Motion_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
Geometric Structure Preserving Warp for Natural Image Stitching | Peng Du, Jifeng Ning, Jiguang Cui, Shaoli Huang, Xinchao Wang, Jiaxin Wang | Preserving geometric structures in the scene plays a vital role in image stitching. However, most of the existing methods ignore the large-scale layouts reflected by straight lines or curves, decreasing overall stitching quality. To address this issue, this work presents a structure-preserving stitching approach that produces images with natural visual effects and less distortion. Our method first employs deep learning-based edge detection to extract various types of large-scale edges. Then, the extracted edges are sampled to construct multiple groups of triangles to represent geometric structures. Meanwhile, a GEometric Structure preserving (GES) energy term is introduced to make these triangles undergo similarity transformation. Further, an optimized GES energy term is presented to reasonably determine the weights of the sampling points on the geometric structure, and the term is added into the Global Similarity Prior (GSP) stitching model called GES-GSP to achieve a smooth transition between local alignment and geometric structure preservation. The effectiveness of GES-GSP is validated through comprehensive experiments on a stitching dataset. The experimental results show that the proposed method outperforms several state-of-the-art methods in geometric structure preservation and obtains more natural stitching results. The code and dataset are available at https://github.com/flowerDuo/GES-GSP-Stitching. | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Geometric_Structure_Preserving_Warp_for_Natural_Image_Stitching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Du_Geometric_Structure_Preserving_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Geometric_Structure_Preserving_Warp_for_Natural_Image_Stitching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Geometric_Structure_Preserving_Warp_for_Natural_Image_Stitching_CVPR_2022_paper.html | CVPR 2022 | null |
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping | Omid Taheri, Vasileios Choutas, Michael J. Black, Dimitrios Tzionas | Generating digital humans that move realistically has many applications and is widely studied, but existing methods focus on the major limbs of the body, ignoring the hands and head. Hands have been separately studied, but the focus has been on generating realistic static grasps of objects. To synthesize virtual characters that interact with the world, we need to generate full-body motions and realistic hand grasps simultaneously. Both sub-problems are challenging on their own and, together, the state space of poses is significantly larger, the scales of hand and body motions differ, and the whole-body posture and the hand grasp must agree, satisfy physical constraints, and be plausible. Additionally, the head is involved because the avatar must look at the object to interact with it. For the first time, we address the problem of generating full-body, hand and head motions of an avatar grasping an unknown object. As input, our method, called GOAL, takes a 3D object, its pose, and a starting 3D body pose and shape. GOAL outputs a sequence of whole-body poses using two novel networks. First, GNet generates a goal whole-body grasp with a realistic body, head, arm, and hand pose, as well as hand-object contact. Second, MNet generates the motion between the starting and goal pose. This is challenging, as it requires the avatar to walk towards the object with foot-ground contact, orient the head towards it, reach out, and grasp it with a realistic hand pose and hand-object contact. To achieve this the networks exploit a representation that combines SMPL-X body parameters and 3D vertex offsets. We train and evaluate GOAL, both qualitatively and quantitatively, on the GRAB dataset. Results show that GOAL generalizes well to unseen objects, outperforming baselines. A perceptual study shows that GOAL's generated motions approach the realism of GRAB's ground truth. GOAL takes a step towards generating realistic full-body object grasping motion. Our models and code are available at https://goal.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2022/papers/Taheri_GOAL_Generating_4D_Whole-Body_Motion_for_Hand-Object_Grasping_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Taheri_GOAL_Generating_4D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11454 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Taheri_GOAL_Generating_4D_Whole-Body_Motion_for_Hand-Object_Grasping_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Taheri_GOAL_Generating_4D_Whole-Body_Motion_for_Hand-Object_Grasping_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Robot Active Mapping via Neural Bipartite Graph Matching | Kai Ye, Siyan Dong, Qingnan Fan, He Wang, Li Yi, Fei Xia, Jue Wang, Baoquan Chen | We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimation to enable more efficient robot movements. Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction. In this paper, we propose a novel algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We reduce the problem to bipartite graph matching, which establishes the node correspondences between two graphs, denoting robots and frontiers. We introduce a multiplex graph neural network (mGNN) that learns the neural distance to fill the affinity matrix for more effective graph matching. We optimize the mGNN with a differentiable linear assignment layer by maximizing the long-term values that favor time efficiency and map completeness via reinforcement learning. We compare our algorithm with several state-of-the-art multi-robot active mapping approaches and adapted reinforcement-learning baselines. Experimental results demonstrate the superior performance and exceptional generalization ability of our algorithm on various indoor scenes and unseen number of robots, when only trained with 9 indoor scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Multi-Robot_Active_Mapping_via_Neural_Bipartite_Graph_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Multi-Robot_Active_Mapping_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16319 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Multi-Robot_Active_Mapping_via_Neural_Bipartite_Graph_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Multi-Robot_Active_Mapping_via_Neural_Bipartite_Graph_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Adversarial Texture for Fooling Person Detectors in the Physical World | Zhanhao Hu, Siyuan Huang, Xiaopei Zhu, Fuchun Sun, Bo Zhang, Xiaolin Hu | Nowadays, cameras equipped with AI systems can capture and analyze images to detect people automatically. However, the AI system can make mistakes when receiving deliberately designed patterns in the real world, i.e., physical adversarial examples. Prior works have shown that it is possible to print adversarial patches on clothes to evade DNN-based person detectors. However, these adversarial examples could have catastrophic drops in the attack success rate when the viewing angle (i.e., the camera's angle towards the object) changes. To perform a multi-angle attack, we propose Adversarial Texture (AdvTexture). AdvTexture can cover clothes with arbitrary shapes so that people wearing such clothes can hide from person detectors from different viewing angles. We propose a generative method, named Toroidal-Cropping-based Expandable Generative Attack (TC-EGA), to craft AdvTexture with repetitive structures. We printed several pieces of cloth with AdvTexure and then made T-shirts, skirts, and dresses in the physical world. Experiments showed that these clothes could fool person detectors in the physical world. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Adversarial_Texture_for_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.03373 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.html | CVPR 2022 | null |
Focal Length and Object Pose Estimation via Render and Compare | Georgy Ponimatkin, Yann Labbé, Bryan Russell, Mathieu Aubry, Josef Sivic | We introduce FocalPose, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are twofold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ponimatkin_Focal_Length_and_Object_Pose_Estimation_via_Render_and_Compare_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ponimatkin_Focal_Length_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ponimatkin_Focal_Length_and_Object_Pose_Estimation_via_Render_and_Compare_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ponimatkin_Focal_Length_and_Object_Pose_Estimation_via_Render_and_Compare_CVPR_2022_paper.html | CVPR 2022 | null |
TO-FLOW: Efficient Continuous Normalizing Flows With Temporal Optimization Adjoint With Moving Speed | Shian Du, Yihong Luo, Wei Chen, Jian Xu, Delu Zeng | Continuous normalizing flows (CNFs) construct invertible mappings between an arbitrary complex distribution and an isotropic Gaussian distribution using Neural Ordinary Differential Equations (neural ODEs). It has not been tractable on large datasets due to the incremental complexity of the neural ODE training. Optimal Transport theory has been applied to regularize the dynamics of the ODE to speed up training in recent works. In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training. In this appoach, we optimize the network weights of the CNF alternately with evolutionary time by coordinate descent. Further with temporal regularization, stability of the evolution is ensured. This approach can be used in conjunction with the original regularization approach. We have experimentally demonstrated that the proposed approach can significantly accelerate training without sacrifying performance over baseline models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_TO-FLOW_Efficient_Continuous_Normalizing_Flows_With_Temporal_Optimization_Adjoint_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Du_TO-FLOW_Efficient_Continuous_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_TO-FLOW_Efficient_Continuous_Normalizing_Flows_With_Temporal_Optimization_Adjoint_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_TO-FLOW_Efficient_Continuous_Normalizing_Flows_With_Temporal_Optimization_Adjoint_With_CVPR_2022_paper.html | CVPR 2022 | null |
Arbitrary-Scale Image Synthesis | Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis, Radu Timofte, Martin Danelljan, Luc Van Gool | Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator's layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ntavelis_Arbitrary-Scale_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ntavelis_Arbitrary-Scale_Image_Synthesis_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02273 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ntavelis_Arbitrary-Scale_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ntavelis_Arbitrary-Scale_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Modal Representation Learning for Zero-Shot Action Recognition | Chung-Ching Lin, Kevin Lin, Lijuan Wang, Zicheng Liu, Linjie Li | We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent. In zero-shot inference, we devise a simple semantic transfer scheme that embeds semantic relatedness information between seen and unseen classes to composite unseen visual prototypes. Accordingly, the discriminative features in the visual structure could be preserved and exploited to alleviate the typical zero-shot issues of information loss, semantic gap, and the hubness problem. Under a rigorous zero-shot setting of not pre-training on additional datasets, the experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets. Code will be made available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Cross-Modal_Representation_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.01657 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Conditional Prompt Learning for Vision-Language Models | Kaiyang Zhou, Jingkang Yang, Chen Change Loy, Ziwei Liu | With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. A recently proposed method named Context Optimization (CoOp) introduces the concept of prompt learning---a recent trend in NLP---to the vision domain for adapting pre-trained vision-language models. Specifically, CoOp turns context words in a prompt into a set of learnable vectors and, with only a few labeled images for learning, can achieve huge improvements over intensively-tuned manual prompts. In our study we identify a critical problem of CoOp: the learned context is not generalizable to wider unseen classes within the same dataset, suggesting that CoOp overfits base classes observed during training. To address the problem, we propose Conditional Context Optimization (CoCoOp), which extends CoOp by further learning a lightweight neural network to generate for each image an input-conditional token (vector). Compared to CoOp's static prompts, our dynamic prompts adapt to each instance and are thus less sensitive to class shift. Extensive experiments show that CoCoOp generalizes much better than CoOp to unseen classes, even showing promising transferability beyond a single dataset; and yields stronger domain generalization performance as well. Code is available at https://github.com/KaiyangZhou/CoOp. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Conditional_Prompt_Learning_for_Vision-Language_Models_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.05557 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Conditional_Prompt_Learning_for_Vision-Language_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Conditional_Prompt_Learning_for_Vision-Language_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification | Shengcai Liao, Ling Shao | Recent studies show that, both explicit deep feature matching as well as large-scale and diverse training data can significantly improve the generalization of person re-identification. However, the efficiency of learning deep matchers on large-scale data has not yet been adequately studied. Though learning with classification parameters or class memory is a popular way, it incurs large memory and computational costs. In contrast, pairwise deep metric learning within mini batches would be a better choice. However, the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. Though online hard example mining has improved the learning efficiency to some extent, the mining in mini batches after random sampling is still limited. This inspires us to explore the use of hard example mining earlier, in the data sampling stage. To do so, in this paper, we propose an efficient mini-batch sampling method, called graph sampling (GS), for large-scale deep metric learning. The basic idea is to build a nearest neighbor relationship graph for all classes at the beginning of each epoch. Then, each mini batch is composed of a randomly selected class and its nearest neighboring classes so as to provide informative and challenging examples for learning. Together with an adapted competitive baseline, we improve the state of the art in generalizable person re-identification significantly, by 25.1% in Rank-1 on MSMT17 when trained on RandPerson. Besides, the proposed method also outperforms the competitive baseline, by 6.8% in Rank-1 on CUHK03-NP when trained on MSMT17. Meanwhile, the training time is significantly reduced, from 25.4 hours to 2 hours when trained on RandPerson with 8,000 identities. Code is available at https://github.com/ShengcaiLiao/QAConv. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liao_Graph_Sampling_Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.01546 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Retrieval-Based Spatially Adaptive Normalization for Semantic Image Synthesis | Yupeng Shi, Xiao Liu, Yuxiang Wei, Zhongqin Wu, Wangmeng Zuo | Semantic image synthesis is a challenging task with many practical applications. Albeit remarkable progress has been made in semantic image synthesis with spatially-adaptive normalization and existing methods normalize the feature activations under the coarse-level guidance (e.g., semantic class). However, different parts of a semantic object (e.g., wheel and window of car) are quite different in structures and textures, making blurry synthesis results usually inevitable due to the missing of fine-grained guidance. In this paper, we propose a novel normalization module, termed as REtrieval-based Spatially AdaptIve normaLization (RESAIL), for introducing pixel level fine-grained guidance to the normalization architecture. Specifically, we first present a retrieval paradigm by finding a content patch of the same semantic class from training set with the most similar shape to each test semantic mask. Then, RESAIL is presented to use the retrieved patch for guiding the feature normalization of corresponding region, and can provide pixel level fine-grained guidance, thereby greatly mitigating blurry synthesis results. Moreover, distorted ground-truth images are also utilized as alternatives of retrieval-based guidance for feature normalization, further benefiting model training and improving visual quality of generated images. Experiments on several challenging datasets show that our RESAIL performs favorably against state-of-the-arts in terms of quantitative metrics, visual quality, and subjective evaluation. The source code and pre-trained models will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_Retrieval-Based_Spatially_Adaptive_Normalization_for_Semantic_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_Retrieval-Based_Spatially_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02854 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Retrieval-Based_Spatially_Adaptive_Normalization_for_Semantic_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Retrieval-Based_Spatially_Adaptive_Normalization_for_Semantic_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Undoing the Damage of Label Shift for Cross-Domain Semantic Segmentation | Yahao Liu, Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, Wen Li | Existing works typically treat cross-domain semantic segmentation(CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional distribution. However, the label shift issue is unfortunately overlooked, which actually commonly exists in the CDSS task, and often causes a classifier bias in the learnt model. In this paper, we give an in-depth analysis and show that the damage of label shift can be overcome by aligning the data conditional distribution and correcting the posterior probability. To this end, we propose a novel approach to undo the damage of the label shift problem in CDSS. In implementation, we adopt class-level feature alignment for conditional distribution alignment, as well as two simple yet effective methods to rectify the classifier bias from source to target by remolding the classifier predictions. We conduct extensive experiments on the benchmark datasets of urban scenes, including GTA5 to Cityscapes and SYNTHIA to Cityscapes, where our proposed approach outperforms previous methods by a large margin. For instance, our model equipped with a self-training strategy reaches 59.3% mIoU on GTA5 to Cityscapes, pushing to a new state-of-the-art. The code will be available at https://github.com/manmanjun/Undoing_UDA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Undoing_the_Damage_of_Label_Shift_for_Cross-Domain_Semantic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.05546 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Undoing_the_Damage_of_Label_Shift_for_Cross-Domain_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Undoing_the_Damage_of_Label_Shift_for_Cross-Domain_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
GPV-Pose: Category-Level Object Pose Estimation via Geometry-Guided Point-Wise Voting | Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xiangyang Ji, Nassir Navab, Federico Tombari | While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications. To circumvent this problem, category-level object pose estimation has recently been revamped, which aims at predicting the 6D pose as well as the 3D metric size for previously unseen instances from a given set of object classes. This is, however, a much more challenging task due to severe intra-class shape variations. To address this issue, we propose GPV-Pose, a novel framework for robust category-level pose estimation, harnessing geometric insights to enhance the learning of category-level pose-sensitive features. First, we introduce a decoupled confidence-driven rotation representation, which allows geometry-aware recovery of the associated rotation matrix. Second, we propose a novel geometry-guided point-wise voting paradigm for robust retrieval of the 3D object bounding box. Finally, leveraging these different output streams, we can enforce several geometric consistency terms, further increasing performance, especially for non-symmetric categories. GPV-Pose produces superior results to state-of-the-art competitors on common public benchmarks, whilst almost achieving real-time inference speed at 20 FPS. Code and trained models will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Di_GPV-Pose_Category-Level_Object_Pose_Estimation_via_Geometry-Guided_Point-Wise_Voting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Di_GPV-Pose_Category-Level_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Di_GPV-Pose_Category-Level_Object_Pose_Estimation_via_Geometry-Guided_Point-Wise_Voting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Di_GPV-Pose_Category-Level_Object_Pose_Estimation_via_Geometry-Guided_Point-Wise_Voting_CVPR_2022_paper.html | CVPR 2022 | null |
Dynamic 3D Gaze From Afar: Deep Gaze Estimation From Temporal Eye-Head-Body Coordination | Soma Nonaka, Shohei Nobuhara, Ko Nishino | We introduce a novel method and dataset for 3D gaze estimation of a freely moving person from a distance, typically in surveillance views. Eyes cannot be clearly seen in such cases due to occlusion and lacking resolution. Existing gaze estimation methods suffer or fall back to approximating gaze with head pose as they primarily rely on clear, close-up views of the eyes. Our key idea is to instead leverage the intrinsic gaze, head, and body coordination of people. Our method formulates gaze estimation as Bayesian prediction given temporal estimates of head and body orientations which can be reliably estimated from a far. We model the head and body orientation likelihoods and the conditional prior of gaze direction on those with separate neural networks which are then cascaded to output the 3D gaze direction. We introduce an extensive new dataset that consists of surveillance videos annotated with 3D gaze directions captured in 5 indoor and outdoor scenes. Experimental results on this and other datasets validate the accuracy of our method and demonstrate that gaze can be accurately estimated from a typical surveillance distance even when the person's face is not visible to the camera. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nonaka_Dynamic_3D_Gaze_From_Afar_Deep_Gaze_Estimation_From_Temporal_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nonaka_Dynamic_3D_Gaze_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nonaka_Dynamic_3D_Gaze_From_Afar_Deep_Gaze_Estimation_From_Temporal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nonaka_Dynamic_3D_Gaze_From_Afar_Deep_Gaze_Estimation_From_Temporal_CVPR_2022_paper.html | CVPR 2022 | null |
Expressive Talking Head Generation With Granular Audio-Visual Control | Borong Liang, Yan Pan, Zhizhi Guo, Hang Zhou, Zhibin Hong, Xiaoguang Han, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang | Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), which controls lip movements, head poses, and facial expressions of a talking head in a granular manner. Our insight is to decouple the audio-visual driving sources through prior-based pre-processing designs. Detailedly, we disassemble the driving image into three complementary parts including: 1) a cropped mouth that facilitates lip-sync; 2) a masked head that implicitly learns pose; and 3) the upper face which works corporately and complementarily with a time-shifted mouth to contribute the expression. Interestingly, the encoded features from the three sources are integrally balanced through reconstruction training. Extensive experiments show that our method generates expressive faces with not only synced mouth shapes, controllable poses, but precisely animated emotional expressions as well. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Expressive_Talking_Head_Generation_With_Granular_Audio-Visual_Control_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Expressive_Talking_Head_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Expressive_Talking_Head_Generation_With_Granular_Audio-Visual_Control_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Expressive_Talking_Head_Generation_With_Granular_Audio-Visual_Control_CVPR_2022_paper.html | CVPR 2022 | null |
Trustworthy Long-Tailed Classification | Bolian Li, Zongbo Han, Haining Li, Huazhu Fu, Changqing Zhang | Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and accordingly unpromising performance especially on tail classes. Recently, the ensembling based methods achieve the state-of-the-art performance and show great potential. However, there are two limitations for current methods. First, their predictions are not trustworthy for failure-sensitive applications. This is especially harmful for the tail classes where the wrong predictions is basically frequent. Second, they assign unified numbers of experts to all samples, which is redundant for easy samples with excessive computational cost. To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework. Our TLC obtains the evidence-based uncertainty (EvU) and evidence for each expert, and then combines these uncertainties and evidences under the Dempster-Shafer Evidence Theory (DST). Moreover, we propose a dynamic expert engagement to reduce the number of engaged experts for easy samples and achieve efficiency while maintaining promising performances. Finally, we conduct comprehensive experiments on the tasks of classification, tail detection, OOD detection and failure prediction. The experimental results show that the proposed TLC outperforms existing methods and is trustworthy with reliable uncertainty. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Trustworthy_Long-Tailed_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Trustworthy_Long-Tailed_Classification_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09030 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Trustworthy_Long-Tailed_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Trustworthy_Long-Tailed_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
Primitive3D: 3D Object Dataset Synthesis From Randomly Assembled Primitives | Xinke Li, Henghui Ding, Zekun Tong, Yuwei Wu, Yeow Meng Chee | Numerous advancements of deep learning can be attributed to access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collection cost. To alleviate this issue, we propose a cost-effective method for automatically generating a large amount of 3D objects with annotations. In particular, we synthesize objects simply by assembling multiple random primitives. These objects are thus auto-annotated with part-based labels originating from primitives. This allows us to perform multi-task learning by combining the supervised segmentation with unsupervised reconstruction. Considering the large overhead of learning on the generated dataset, we further propose a dataset distillation strategy to remove redundant samples regarding a target dataset. We conduct extensive experiments for the downstream tasks of 3D object classification. The results indicate that our dataset, together with multi-task pretraining on its annotations, achieves the best performance compared to other commonly used datasets. Further study suggests that our strategy can improve the model performance by pretraining and fine-tuning scheme, especially for a dataset with a small scale. In addition, pretraining with the proposed dataset distillation method can save 86% of the pretraining time with negligible performance degradation. We expect that our attempt provides a new data-centric perspective for training 3D deep models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Primitive3D_3D_Object_Dataset_Synthesis_From_Randomly_Assembled_Primitives_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Primitive3D_3D_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.12627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Primitive3D_3D_Object_Dataset_Synthesis_From_Randomly_Assembled_Primitives_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Primitive3D_3D_Object_Dataset_Synthesis_From_Randomly_Assembled_Primitives_CVPR_2022_paper.html | CVPR 2022 | null |
Mix and Localize: Localizing Sound Sources in Mixtures | Xixi Hu, Ziyang Chen, Andrew Owens | We present a method for simultaneously localizing multiple sound sources within a visual scene. This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal. Our method jointly solves both tasks at once, using a formulation inspired by the contrastive random walk of Jabri et al. We create a graph in which images and separated sounds each correspond to nodes, and train a random walker to transition between nodes from different modalities with high return probability. The transition probabilities for this walk are determined by an audio-visual similarity metric that is learned by our model. We show through experiments with musical instruments and human speech that our model can successfully localize multiple sounds, outperforming other self-supervised methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Mix_and_Localize_Localizing_Sound_Sources_in_Mixtures_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Mix_and_Localize_Localizing_Sound_Sources_in_Mixtures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Mix_and_Localize_Localizing_Sound_Sources_in_Mixtures_CVPR_2022_paper.html | CVPR 2022 | null |
FisherMatch: Semi-Supervised Rotation Regression via Entropy-Based Filtering | Yingda Yin, Yingcheng Cai, He Wang, Baoquan Chen | Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. Recent works achieve good performance relying on a large amount of expensive-to-obtain labeled data. To reduce the amount of supervision, we for the first time propose a general framework, FisherMatch, for semi-supervised rotation regression, without assuming any domain-specific knowledge or paired data. Inspired by the popular semi-supervised approach, FixMatch, we propose to leverage pseudo label filtering to facilitate the information flow from labeled data to unlabeled data in a teacher-student mutual learning framework. However, incorporating the pseudo label filtering mechanism into semi-supervised rotation regression is highly non-trivial, mainly due to the lack of a reliable confidence measure for rotation prediction. In this work, we propose to leverage matrix Fisher distribution to build a probabilistic model of rotation and devise a matrix Fisher-based regressor for jointly predicting rotation along with its prediction uncertainty. We then propose to use the entropy of the predicted distribution as a confidence measure, which enables us to perform pseudo label filtering for rotation regression. For supervising such distribution-like pseudo labels, we further investigate the problem of how to enforce loss between two matrix Fisher distributions. Our extensive experiments show that our method can work well even under very low labeled data ratios on different benchmarks, achieving significant and consistent performance improvement over supervised learning and other semi-supervised learning baselines. Our project page is at https://yd-yin.github.io/FisherMatch. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yin_FisherMatch_Semi-Supervised_Rotation_Regression_via_Entropy-Based_Filtering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yin_FisherMatch_Semi-Supervised_Rotation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15765 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_FisherMatch_Semi-Supervised_Rotation_Regression_via_Entropy-Based_Filtering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_FisherMatch_Semi-Supervised_Rotation_Regression_via_Entropy-Based_Filtering_CVPR_2022_paper.html | CVPR 2022 | null |
NPBG++: Accelerating Neural Point-Based Graphics | Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Burnaev | We present a new system (NPBG++) for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently leverages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graphics in several important ways. By predicting the descriptors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suitable for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rakhimov_NPBG_Accelerating_Neural_Point-Based_Graphics_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rakhimov_NPBG_Accelerating_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rakhimov_NPBG_Accelerating_Neural_Point-Based_Graphics_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rakhimov_NPBG_Accelerating_Neural_Point-Based_Graphics_CVPR_2022_paper.html | CVPR 2022 | null |
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis | Tianyi Chen, Yunfei Zhang, Xiaoyang Huo, Si Wu, Yong Xu, Hau San Wong | Generative Adversarial Network (GAN)-based models have greatly facilitated image synthesis. However, the model performance may be degraded when applied to fine-grained data, due to limited training samples and subtle distinction among categories. Different from generic GANs, we address the issue from a new perspective of discovering and utilizing the underlying structure of real data to explicitly regularize the spatial organization of latent space. To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN. By projecting random vectors drawn from a prior distribution onto a hyper-sphere, we can model more complex distributions, while at the same time the similarity between the resulting latent vectors depends only on the angle, but not on their magnitudes. On the other hand, we also incorporate a mapping network to map real images onto the hyper-sphere, and match latent vectors with the underlying structure of real data via real-fake cluster alignment. As a result, we obtain a spatially organized latent space, which is useful for capturing class-independent variation factors. The experimental results suggest that our SphericGAN achieves state-of-the-art performance in synthesizing high-fidelity images with precise class semantics. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_SphericGAN_Semi-Supervised_Hyper-Spherical_Generative_Adversarial_Networks_for_Fine-Grained_Image_Synthesis_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_SphericGAN_Semi-Supervised_Hyper-Spherical_Generative_Adversarial_Networks_for_Fine-Grained_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_SphericGAN_Semi-Supervised_Hyper-Spherical_Generative_Adversarial_Networks_for_Fine-Grained_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
HairMapper: Removing Hair From Portraits Using GANs | Yiqian Wu, Yong-Liang Yang, Xiaogang Jin | Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair. To this end, we present a dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs). Our core idea is to train a fully connected network HairMapper to find the direction of hair removal in the latent space of StyleGAN for the training stage. We develop a new separation boundary and diffuse method to generate paired training data for males, and a novel "female-male-bald" pipeline for paired data of females. Experiments show that our method can naturally deal with portrait images with variations on gender, age, etc. We validate the superior performance of our method by comparing it to state-of-the-art methods through extensive experiments and user studies. We also demonstrate its applications in hair design and 3D face reconstruction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_HairMapper_Removing_Hair_From_Portraits_Using_GANs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_HairMapper_Removing_Hair_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_HairMapper_Removing_Hair_From_Portraits_Using_GANs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_HairMapper_Removing_Hair_From_Portraits_Using_GANs_CVPR_2022_paper.html | CVPR 2022 | null |
Affine Medical Image Registration With Coarse-To-Fine Vision Transformer | Tony C. W. Mok, Albert C. S. Chung | Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone performance of the affine subnetwork is less explored. Moreover, existing CNN-based affine registration approaches focus either on the local misalignment or the global orientation and position of the input to predict the affine transformation matrix, which are sensitive to spatial initialization and exhibit limited generalizability apart from the training dataset. In this paper, we present a fast and robust learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method naturally leverages the global connectivity and locality of the convolutional vision transformer and the multi-resolution strategy to learn the global affine registration. We evaluate our method on 3D brain atlas registration and template-matching normalization. Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods. The source code is available at https://github.com/cwmok/C2FViT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mok_Affine_Medical_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
SMPL-A: Modeling Person-Specific Deformable Anatomy | Hengtao Guo, Benjamin Planche, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu | A variety of diagnostic and therapeutic protocols rely on locating in vivo target anatomical structures, which can be obtained from medical scans. However, organs move and deform as the patient changes his/her pose. In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e.g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment. Such custom proxy methods are typically sub-optimal, constraining the clinicians and costing precious time and money to the patients. To the best of our knowledge, this work is the first to present a learning-based approach to estimate the patient's internal organ deformation for arbitrary human poses in order to assist with radiotherapy and similar medical protocols. The underlying method first leverages medical scans to learn a patient-specific representation that potentially encodes the organ's shape and elastic properties. During inference, given the patient's current body pose information and the organ's representation extracted from previous medical scans, our method can estimate their current organ deformation to offer guidance to clinicians. We conduct experiments on a well-sized dataset which is augmented through real clinical data using finite element modeling. Our results suggest that pose-dependent organ deformation can be learned through a point cloud autoencoder conditioned on the parametric pose input. We hope that this work can be a starting point for future research towards closing the loop between human mesh recovery and anatomical reconstruction, with applications beyond the medical domain. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_SMPL-A_Modeling_Person-Specific_Deformable_Anatomy_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_SMPL-A_Modeling_Person-Specific_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_SMPL-A_Modeling_Person-Specific_Deformable_Anatomy_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_SMPL-A_Modeling_Person-Specific_Deformable_Anatomy_CVPR_2022_paper.html | CVPR 2022 | null |
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding | Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, Chongyi Li | Despite single image dehazing has been made promising progress with Convolutional Neural Networks (CNNs), the inherent equivariance and locality of convolution still bottleneck dehazing performance. Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance. The key insight of this study is to investigate how to combine CNN and Transformer for image dehazing. To solve the feature inconsistency issue between Transformer and CNN, we propose to modulate CNN features via learning modulation matrices (i.e., coefficient matrix and bias matrix) conditioned on Transformer features instead of simple feature addition or concatenation. The feature modulation naturally inherits the global context modeling capability of Transformer and the local representation capability of CNN. We bring a haze density-related prior into Transformer via a novel transmission-aware 3D position embedding module, which not only provides the relative position but also suggests the haze density of different spatial regions. Extensive experiments demonstrate that our method, DeHamer, attains state-of-the-art performance on several image dehazing benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Image_Dehazing_Transformer_With_Transmission-Aware_3D_Position_Embedding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Image_Dehazing_Transformer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Image_Dehazing_Transformer_With_Transmission-Aware_3D_Position_Embedding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Image_Dehazing_Transformer_With_Transmission-Aware_3D_Position_Embedding_CVPR_2022_paper.html | CVPR 2022 | null |
Out-of-Distribution Generalization With Causal Invariant Transformations | Ruoyu Wang, Mingyang Yi, Zhitang Chen, Shengyu Zhu | In real-world applications, it is important and desirable to learn a model that performs well on out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the OOD generalization problem, with the idea resting on the causal mechanism that is invariant across domains of interest. To leverage the generally unknown causal mechanism, existing works assume a linear form of causal feature or require sufficiently many and diverse training domains, which are usually restrictive in practice. In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature. Our approach is based on transformations that modify the non-causal feature but leave the causal part unchanged, which can be either obtained from prior knowledge or learned from the training data in the multi-domain scenario. Under the setting of invariant causal mechanism, we theoretically show that if all such transformations are available, then we can learn a minimax optimal model across the domains using only single domain data. Noticing that knowing a complete set of these causal invariant transformations may be impractical, we further show that it suffices to know only a subset of these transformations. Based on the theoretical findings, a regularized training procedure is proposed to improve the OOD generalization capability. Extensive experimental results on both synthetic and real datasets verify the effectiveness of the proposed algorithm, even with only a few causal invariant transformations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Out-of-Distribution_Generalization_With_Causal_Invariant_Transformations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Out-of-Distribution_Generalization_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Out-of-Distribution_Generalization_With_Causal_Invariant_Transformations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Out-of-Distribution_Generalization_With_Causal_Invariant_Transformations_CVPR_2022_paper.html | CVPR 2022 | null |
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap | Jinke Li, Xiao He, Yang Wen, Yuan Gao, Xiaoqiang Cheng, Dan Zhang | As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation. However, in terms of speed and accuracy, existing LiDAR methods in the field are still limited. In this paper, we propose a fast and high-performance LiDAR-based framework, referred to as Panoptic-PHNet, with three attractive aspects: 1) We introduce a clustering pseudo heatmap as a new paradigm, which, followed by a center grouping module, yields instance centers for efficient clustering without object-level learning tasks. 2) A knn-transformer module is proposed to model the interaction among foreground points for accurate offset regression. 3) For backbone design, we fuse the fine-grained voxel features and the 2D Bird's Eye View (BEV) features with different receptive fields to utilize both detailed and global information. Extensive experiments on both SemanticKITTI dataset and nuScenes dataset show that our Panoptic-PHNet surpasses state-of-the-art methods by remarkable margins with a real-time speed. We achieve the 1st place on the public leaderboard of SemanticKITTI and leading performance on the recently released leaderboard of nuScenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Panoptic-PHNet_Towards_Real-Time_and_High-Precision_LiDAR_Panoptic_Segmentation_via_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Panoptic-PHNet_Towards_Real-Time_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Panoptic-PHNet_Towards_Real-Time_and_High-Precision_LiDAR_Panoptic_Segmentation_via_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Panoptic-PHNet_Towards_Real-Time_and_High-Precision_LiDAR_Panoptic_Segmentation_via_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
Dual-Key Multimodal Backdoors for Visual Question Answering | Matthew Walmer, Karan Sikka, Indranil Sur, Abhinav Shrivastava, Susmit Jha | The success of deep learning has enabled advances in multimodal tasks that require non-trivial fusion of multiple input domains. Although multimodal models have shown potential in many problems, their increased complexity makes them more vulnerable to attacks. A Backdoor (or Trojan) attack is a class of security vulnerability wherein an attacker embeds a malicious secret behavior into a network (e.g. targeted misclassification) that is activated when an attacker-specified trigger is added to an input. In this work, we show that multimodal networks are vulnerable to a novel type of attack that we refer to as Dual-Key Multimodal Backdoors. This attack exploits the complex fusion mechanisms used by state-of-the-art networks to embed backdoors that are both effective and stealthy. Instead of using a single trigger, the proposed attack embeds a trigger in each of the input modalities and activates the malicious behavior only when both the triggers are present. We present an extensive study of multimodal backdoors on the Visual Question Answering (VQA) task with multiple architectures and visual feature backbones. A major challenge in embedding backdoors in VQA models is that most models use visual features extracted from a fixed pretrained object detector. This is challenging for the attacker as the detector can distort or ignore the visual trigger entirely, which leads to models where backdoors are over-reliant on the language trigger. We tackle this problem by proposing a visual trigger optimization strategy designed for pretrained object detectors. Through this method, we create Dual-Key Backdoors with over a 98% attack success rate while only poisoning 1% of the training data. Finally, we release TrojVQA, a large collection of clean and trojan VQA models to enable research in defending against multimodal backdoors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Walmer_Dual-Key_Multimodal_Backdoors_for_Visual_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Walmer_Dual-Key_Multimodal_Backdoors_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.07668 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Walmer_Dual-Key_Multimodal_Backdoors_for_Visual_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Walmer_Dual-Key_Multimodal_Backdoors_for_Visual_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
A Differentiable Two-Stage Alignment Scheme for Burst Image Reconstruction With Large Shift | Shi Guo, Xi Yang, Jianqi Ma, Gaofeng Ren, Lei Zhang | Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. State-of-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-to-end manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available at https://github.com/GuoShi28/2StageAlign. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_A_Differentiable_Two-Stage_Alignment_Scheme_for_Burst_Image_Reconstruction_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_A_Differentiable_Two-Stage_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09294 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_A_Differentiable_Two-Stage_Alignment_Scheme_for_Burst_Image_Reconstruction_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_A_Differentiable_Two-Stage_Alignment_Scheme_for_Burst_Image_Reconstruction_With_CVPR_2022_paper.html | CVPR 2022 | null |
Unifying Panoptic Segmentation for Autonomous Driving | Oliver Zendel, Matthias Schörghuber, Bernhard Rainer, Markus Murschitz, Csaba Beleznai | This paper aims to improve panoptic segmentation for real-world applications in three ways. First, we present a label policy that unifies four of the most popular panoptic segmentation datasets for autonomous driving. We also clean up label confusion by adding the new vehicle labels pickup and van. Full relabeling information for the popular Mapillary Vistas, IDD, and Cityscapes dataset are provided to add these new labels to existing setups. Second, we introduce Wilddash2 (WD2), a new dataset and public benchmark service for panoptic segmentation. The dataset consists of more than 5000 unique driving scenes from all over the world with a focus on visually challenging scenes, such as diverse weather conditions, lighting situations, and camera characteristics. We showcase experimental visual hazard classifiers which help to pre-filter challenging frames during dataset creation. Finally, to characterize the robustness of algorithms in out-of-distribution situations, we introduce hazard-aware and negative testing for panoptic segmentation as well as statistical significance calculations that increase confidence for both concepts. Additionally, we present a novel technique for visualizing panoptic segmentation errors. Our experiments show the negative impact of visual hazards on panoptic segmentation quality. Additional data from the WD2 dataset improves performance for visually challenging scenes and thus robustness in real-world scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zendel_Unifying_Panoptic_Segmentation_for_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zendel_Unifying_Panoptic_Segmentation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zendel_Unifying_Panoptic_Segmentation_for_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zendel_Unifying_Panoptic_Segmentation_for_Autonomous_Driving_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans From a Single Camera | Jae Shin Yoon, Duygu Ceylan, Tuanfeng Y. Wang, Jingwan Lu, Jimei Yang, Zhixin Shu, Hyun Soo Park | Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i.e., there exists a number of cloth geometric configurations given a pose depending on the way it has moved. Such appearance modeling conditioned on motion has been largely neglected in existing human rendering methods, resulting in rendering of physically implausible motion. A key challenge of learning the dynamics of the appearance lies in the requirement of a prohibitively large amount of observations. In this paper, we present a compact motion representation by enforcing equivariance---a representation is expected to be transformed in the way that the pose is transformed. We model an equivariant encoder that can generate the generalizable representation from the spatial and temporal derivatives of the 3D body surface. This learned representation is decoded by a compositional multi-task decoder that renders high fidelity time-varying appearance. Our experiments show that our method can generate a temporally coherent video of dynamic humans for unseen body poses and novel views given a single view video. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yoon_Learning_Motion-Dependent_Appearance_for_High-Fidelity_Rendering_of_Dynamic_Humans_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yoon_Learning_Motion-Dependent_Appearance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12780 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yoon_Learning_Motion-Dependent_Appearance_for_High-Fidelity_Rendering_of_Dynamic_Humans_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yoon_Learning_Motion-Dependent_Appearance_for_High-Fidelity_Rendering_of_Dynamic_Humans_From_CVPR_2022_paper.html | CVPR 2022 | null |
On the Road to Online Adaptation for Semantic Image Segmentation | Riccardo Volpi, Pau De Jorge, Diane Larlus, Gabriela Csurka | We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goal is fostering the development of adaptive learning systems that will continuously learn, without supervision, in ever-changing environments. Typical protocols that study adaptation algorithms for segmentation models are limited to few domains, adaptation happens offline, and human intervention is generally required, at least to annotate data for hyper-parameter tuning. We argue that such constraints are incompatible with algorithms that can continuously adapt to different real-world situations. To address this, we propose a protocol where models need to learn online, from sequences of temporally correlated images, requiring continuous, frame-by-frame adaptation. We accompany this new protocol with a variety of baselines to tackle the proposed formulation, as well as an extensive analysis of their behaviors, which can serve as a starting point for future research. | https://openaccess.thecvf.com/content/CVPR2022/papers/Volpi_On_the_Road_to_Online_Adaptation_for_Semantic_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Volpi_On_the_Road_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Volpi_On_the_Road_to_Online_Adaptation_for_Semantic_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Volpi_On_the_Road_to_Online_Adaptation_for_Semantic_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes | Jon Donnelly, Alina Jade Barnett, Chaofan Chen | We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yielding explanations in the form of "this looks like that." However, while previous methods use spatially rigid prototypes, we address this shortcoming by proposing spatially flexible prototypes. Each prototype is made up of several prototypical parts that adaptively change their relative spatial positions depending on the input image. Consequently, a Deformable ProtoPNet can explicitly capture pose variations and context, improving both model accuracy and the richness of explanations provided. Compared to other case-based interpretable models using prototypes, our approach achieves state-of-the-art accuracy and gives an explanation with greater context. The code is available at https://github.com/jdonnelly36/Deformable-ProtoPNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Donnelly_Deformable_ProtoPNet_An_Interpretable_Image_Classifier_Using_Deformable_Prototypes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Donnelly_Deformable_ProtoPNet_An_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15000 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Donnelly_Deformable_ProtoPNet_An_Interpretable_Image_Classifier_Using_Deformable_Prototypes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Donnelly_Deformable_ProtoPNet_An_Interpretable_Image_Classifier_Using_Deformable_Prototypes_CVPR_2022_paper.html | CVPR 2022 | null |
Context-Aware Sequence Alignment Using 4D Skeletal Augmentation | Taein Kwon, Bugra Tekin, Siyu Tang, Marc Pollefeys | Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality. State-of-the-art methods directly learn image-based embedding space by leveraging powerful deep convolutional neural networks. While being straightforward, their results are far from satisfactory, the aligned videos exhibit severe temporal discontinuity without additional post-processing steps. The recent advancements in human body and hand pose estimation in the wild promise new ways of addressing the task of human action alignment in videos. In this work, based on off-the-shelf human pose estimators, we propose a novel context-aware self-supervised learning architecture to align sequences of actions. We name it CASA. Specifically, CASA employs self-attention and cross-attention mechanisms to incorporate the spatial and temporal context of human actions, which can solve the temporal discontinuity problem. Moreover, we introduce a self-supervised learning scheme that is empowered by novel 4D augmentation techniques for 3D skeleton representations. We systematically evaluate the key components of our method. Our experiments on three public datasets demonstrate CASA significantly improves phase progress and Kendall's Tau scores over the previous state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kwon_Context-Aware_Sequence_Alignment_Using_4D_Skeletal_Augmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kwon_Context-Aware_Sequence_Alignment_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.12223 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kwon_Context-Aware_Sequence_Alignment_Using_4D_Skeletal_Augmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kwon_Context-Aware_Sequence_Alignment_Using_4D_Skeletal_Augmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Perturbed and Strict Mean Teachers for Semi-Supervised Semantic Segmentation | Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, Gustavo Carneiro | Consistency learning using input image, feature, or network perturbations has shown remarkable results in semi-supervised semantic segmentation, but this approach can be seriously affected by inaccurate predictions of unlabelled training images. There are two consequences of these inaccurate predictions: 1) the training based on the "strict" cross-entropy (CE) loss can easily overfit prediction mistakes, leading to confirmation bias; and 2) the perturbations applied to these inaccurate predictions will use potentially erroneous predictions as training signals, degrading consistency learning. In this paper, we address the prediction accuracy problem of consistency learning methods with novel extensions of the mean-teacher (MT) model, which include a new auxiliary teacher, and the replacement of MT's mean square error (MSE) by a stricter confidence-weighted cross-entropy (Conf-CE) loss. The accurate prediction by this model allows us to use a challenging combination of network, input data and feature perturbations to improve the consistency learning generalisation, where the feature perturbations consist of a new adversarial perturbation. Results on public benchmarks show that our approach achieves remarkable improvements over the previous SOTA methods in the field. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Perturbed_and_Strict_Mean_Teachers_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Perturbed_and_Strict_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Perturbed_and_Strict_Mean_Teachers_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Perturbed_and_Strict_Mean_Teachers_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Motion-Modulated Temporal Fragment Alignment Network for Few-Shot Action Recognition | Jiamin Wu, Tianzhu Zhang, Zhe Zhang, Feng Wu, Yongdong Zhang | While the majority of FSL models focus on image classification, the extension to action recognition is rather challenging due to the additional temporal dimension in videos. To address this issue, we propose an end-to-end Motion-modulated Temporal Fragment Alignment Network (MTFAN) by jointly exploring the task-specific motion modulation and the multi-level temporal fragment alignment for Few-Shot Action Recognition (FSAR). The proposed MTFAN model enjoys several merits. First, we design a motion modulator conditioned on the learned task-specific motion embeddings, which can activate the channels related to the task-shared motion patterns for each frame. Second, a segment attention mechanism is proposed to automatically discover the higher-level segments for multi-level temporal fragment alignment, which encompasses the frame-to-frame, segment-to-segment, and segment-to-frame alignments. To the best of our knowledge, this is the first work to exploit task-specific motion modulation for FSAR. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against the state-of-the-art FSAR methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Motion-Modulated_Temporal_Fragment_Alignment_Network_for_Few-Shot_Action_Recognition_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Motion-Modulated_Temporal_Fragment_Alignment_Network_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Motion-Modulated_Temporal_Fragment_Alignment_Network_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Focal Sparse Convolutional Networks for 3D Object Detection | Yukang Chen, Yanwei Li, Xiangyu Zhang, Jian Sun, Jiaya Jia | Non-uniformed 3D sparse data, e.g., point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic components in sparse convolutional networks (Sparse CNNs) process all sparse data, regardless of regular or submanifold sparse convolution. In this paper, we introduce two new modules to enhance the capability of Sparse CNNs, both are based on making feature sparsity learnable with position-wise importance prediction. They are focal sparse convolution (Focals Conv) and its multi-modal variant of focal sparse convolution with fusion, or Focals Conv-F for short. The new modules can readily substitute their plain counterparts in existing Sparse CNNs and be jointly trained in an end-to-end fashion. For the first time, we show that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection. Extensive experiments on the KITTI, nuScenes and Waymo benchmarks validate the effectiveness of our approach. Without bells and whistles, our results outperform all existing single-model entries on the nuScenes test benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Focal_Sparse_Convolutional_Networks_for_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Focal_Sparse_Convolutional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Focal_Sparse_Convolutional_Networks_for_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Focal_Sparse_Convolutional_Networks_for_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Masked Autoencoders Are Scalable Vision Learners | Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick | This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pre-training and shows promising scaling behavior. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Masked_Autoencoders_Are_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.06377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.html | CVPR 2022 | null |
Point-BERT: Pre-Training 3D Point Cloud Transformers With Masked Point Modeling | Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, Jiwen Lu | We present Point-BERT, a novel paradigm for learning Transformers to generalize the concept of BERT onto 3D point cloud. Following BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a point cloud into several local patches, and a point cloud Tokenizer is devised via a discrete Variational AutoEncoder (dVAE) to generate discrete point tokens containing meaningful local information. Then, we randomly mask some patches of input point clouds and feed them into the backbone Transformer. The pre-training objective is to recover the original point tokens at the masked locations under the supervision of point tokens obtained by the Tokenizer. Extensive experiments demonstrate that the proposed BERT-style pre-training strategy significantly improves the performance of standard point cloud Transformers. Equipped with our pre-training strategy, we show that a pure Transformer architecture attains 93.7% accuracy on ModelNet40 and 83.1% accuracy on the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs and human priors. We also demonstrate that the representations learned by Point-BERT transfer well to new tasks and domains, where our models largely advance the state-of-the-art of few-shot point cloud classification task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Point-BERT_Pre-Training_3D_Point_Cloud_Transformers_With_Masked_Point_Modeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Point-BERT_Pre-Training_3D_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Point-BERT_Pre-Training_3D_Point_Cloud_Transformers_With_Masked_Point_Modeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Point-BERT_Pre-Training_3D_Point_Cloud_Transformers_With_Masked_Point_Modeling_CVPR_2022_paper.html | CVPR 2022 | null |
Nested Collaborative Learning for Long-Tailed Visual Recognition | Jun Li, Zichang Tan, Jun Wan, Zhen Lei, Guodong Guo | The networks trained on the long-tailed dataset vary remarkably, despite the same training settings, which shows the great uncertainty in long-tailed learning. To alleviate the uncertainty, we propose a Nested Collaborative Learning (NCL), which tackles the problem by collaboratively learning multiple experts together. NCL consists of two core components, namely Nested Individual Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on the individual supervised learning for each single expert and the knowledge transferring among multiple experts, respectively. To learn representations more thoroughly, both NIL and NBOD are formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Regarding the learning in the partial perspective, we specifically select the negative categories with high predicted scores as the hard categories by using a proposed Hard Category Mining (HCM). In the NCL, the learning from two perspectives is nested, highly related and complementary, and helps the network to capture not only global and robust features but also meticulous distinguishing ability. Moreover, self-supervision is further utilized for feature enhancement. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether by using a single model or an ensemble. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Nested_Collaborative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Nested_Collaborative_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Nested_Collaborative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Nested_Collaborative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Crowd Counting in the Frequency Domain | Weibo Shu, Jia Wan, Kay Chen Tan, Sam Kwong, Antoni B. Chan | This paper investigates crowd counting in the frequency domain, which is a novel direction compared to the traditional view in the spatial domain. By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that is simple, effective, and efficient. The solid theoretical analysis ends up as an implementation-friendly loss function, which requires only standard tensor operations in the training process. We prove that our loss function is an upper bound of the pseudo sup norm metric between the ground truth and the prediction density map (over all of their sub-regions), and demonstrate its efficacy and efficiency versus other loss functions. The experimental results also show its competitiveness to the state-of-the-art on five benchmark data sets: ShanghaiTech A and B, UCF-QNRF, JHU++, and NWPU. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shu_Crowd_Counting_in_the_Frequency_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shu_Crowd_Counting_in_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Crowd_Counting_in_the_Frequency_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Crowd_Counting_in_the_Frequency_Domain_CVPR_2022_paper.html | CVPR 2022 | null |
Restormer: Efficient Transformer for High-Resolution Image Restoration | Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang | Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown significant performance gains on natural language and high-level vision tasks. While the Transformer model mitigates the shortcomings of CNNs (i.e., limited receptive field and inadaptability to input content), its computational complexity grows quadratically with the spatial resolution, therefore making it infeasible to apply to most image restoration tasks involving high-resolution images. In this work, we propose an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images. Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks, including image deraining, single-image motion deblurring, defocus deblurring (single-image and dual-pixel data), and image denoising (Gaussian grayscale/color denoising, and real image denoising). The source code and pre-trained models are available at https://github.com/swz30/Restormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zamir_Restormer_Efficient_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09881 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction | Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Wen Gao | Although many video prediction methods have obtained good performance in low-resolution (64 128) videos, predictive models for high-resolution (512 4K) videos have not been fully explored yet, which are more meaningful due to the increasing demand for high-quality videos. Compared with low-resolution videos, high-resolution videos contain richer appearance (spatial) information and more complex motion (temporal) information. In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction. On the one hand, we propose a Spatiotemporal Encoding-Decoding Scheme to preserve more spatiotemporal information for high-resolution videos. In this way, the appearance details for each frame can be greatly preserved. On the other hand, we design a Residual Predictive Memory (RPM) which focuses on modeling the spatiotemporal residual features (STRF) between previous and future frames instead of the whole frame, which can greatly help capture the complex motion information in high-resolution videos. In addition, the proposed RPM can supervise the spatial encoder and temporal encoder to extract different features in the spatial domain and the temporal domain, respectively. Moreover, the proposed model is trained using generative adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve the perceptual quality of the predictions. Experimental results show that STRPM can generate more satisfactory results compared with various existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_STRPM_A_Spatiotemporal_Residual_Predictive_Model_for_High-Resolution_Video_Prediction_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_STRPM_A_Spatiotemporal_Residual_Predictive_Model_for_High-Resolution_Video_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_STRPM_A_Spatiotemporal_Residual_Predictive_Model_for_High-Resolution_Video_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Learning From Untrimmed Videos: Self-Supervised Video Representation Learning With Hierarchical Consistency | Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang | Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visual patterns and limited performance gain. In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos. To this end, we propose to learn a hierarchy of consistencies in videos, i.e., visual consistency and topical consistency, corresponding respectively to clip pairs that tend to be visually similar when separated by a short time span and share similar topics when separated by a long time span. Specifically, a hierarchical consistency learning framework HiCo is presented, where the visually consistent pairs are encouraged to have the same representation through contrastive learning, while the topically consistent pairs are coupled through a topical classifier that distinguishes whether they are topicrelated. Further, we impose a gradual sampling algorithm for proposed hierarchical consistency learning, and demonstrate its theoretical superiority. Empirically, we show that not only HiCo can generate stronger representations on untrimmed videos, it also improves the representation quality when applied to trimmed videos. This is in contrast to standard contrastive learning that fails to learn appropriate representations from untrimmed videos. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qing_Learning_From_Untrimmed_Videos_Self-Supervised_Video_Representation_Learning_With_Hierarchical_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qing_Learning_From_Untrimmed_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03017 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qing_Learning_From_Untrimmed_Videos_Self-Supervised_Video_Representation_Learning_With_Hierarchical_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qing_Learning_From_Untrimmed_Videos_Self-Supervised_Video_Representation_Learning_With_Hierarchical_CVPR_2022_paper.html | CVPR 2022 | null |
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning With Pairwise Alignment | Zhipeng Ding, Marc Niethammer | Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) images are warped into an atlas space to study intra-subject or inter-subject variations or (2) a possibly probabilistic atlas is warped into image space to assign anatomical labels. Atlas estimation and nonparametric transformations are computationally expensive as they usually require numerical optimization. Additionally, previous approaches for atlas building often define similarity measures between a fuzzy atlas and each individual image, which may cause alignment difficulties because a fuzzy atlas does not exhibit clear anatomical structures in contrast to the individual images. This work explores using a convolutional neural network (CNN) to jointly predict the atlas and a stationary velocity field (SVF) parameterization for diffeomorphic image registration with respect to the atlas. Our approach does not require affine pre-registrations and utilizes pairwise image alignment losses to increase registration accuracy. We evaluate our model on 3D knee magnetic resonance images (MRI) from the OAI-ZIB dataset. Our results show that the proposed framework achieves better performance than other state-of-the-art image registration algorithms, allows for end-to-end training, and for fast inference at test time. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Aladdin_Joint_Atlas_Building_and_Diffeomorphic_Registration_Learning_With_Pairwise_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Aladdin_Joint_Atlas_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.03563 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Aladdin_Joint_Atlas_Building_and_Diffeomorphic_Registration_Learning_With_Pairwise_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Aladdin_Joint_Atlas_Building_and_Diffeomorphic_Registration_Learning_With_Pairwise_CVPR_2022_paper.html | CVPR 2022 | null |
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation | Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, Jie Yang | Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications. In this work, we devise an efficient encoder-decoder based network, termed IFRNet, for fast intermediate frame synthesizing. It first extracts pyramid features from given inputs, and then refines the bilateral intermediate flow fields together with a powerful intermediate feature until generating the desired output. The gradually refined intermediate feature can not only facilitate intermediate flow estimation, but also compensate for contextual details, making IFRNet do not need additional synthesis or refinement module. To fully release its potential, we further propose a novel task-oriented optical flow distillation loss to focus on learning the useful teacher knowledge towards frame synthesizing. Meanwhile, a new geometry consistency regularization term is imposed on the gradually refined intermediate features to keep better structure layout. Experiments on various benchmarks demonstrate the excellent performance and fast inference speed of proposed approaches. Code is available at https://github.com/ltkong218/IFRNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_IFRNet_Intermediate_Feature_Refine_Network_for_Efficient_Frame_Interpolation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_IFRNet_Intermediate_Feature_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.14620 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_IFRNet_Intermediate_Feature_Refine_Network_for_Efficient_Frame_Interpolation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_IFRNet_Intermediate_Feature_Refine_Network_for_Efficient_Frame_Interpolation_CVPR_2022_paper.html | CVPR 2022 | null |
Large Loss Matters in Weakly Supervised Multi-Label Classification | Youngwook Kim, Jae Myung Kim, Zeynep Akata, Jungwoo Lee | Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly important due to its huge annotation cost. In this work, we first regard unobserved labels as negative labels, casting the WSML task into noisy multi-label classification. From this point of view, we empirically observe that memorization effect, which was first discovered in a noisy multi-class setting, also occurs in a multi-label setting. That is, the model first learns the representation of clean labels, and then starts memorizing noisy labels. Based on this finding, we propose novel methods for WSML which reject or correct the large loss samples to prevent model from memorizing the noisy label. Without heavy and complex components, our proposed methods outperform previous state-of-the-art WSML methods on several partial label settings including Pascal VOC 2012, MS COCO, NUSWIDE, CUB, and OpenImages V3 datasets. Various analysis also show that our methodology actually works well, validating that treating large loss properly matters in a weakly supervised multi-label classification. Our code is available at https://github.com/snucml/LargeLossMatters. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Large_Loss_Matters_in_Weakly_Supervised_Multi-Label_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Large_Loss_Matters_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Large_Loss_Matters_in_Weakly_Supervised_Multi-Label_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Large_Loss_Matters_in_Weakly_Supervised_Multi-Label_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
Toward Practical Monocular Indoor Depth Estimation | Cho-Ying Wu, Jialiang Wang, Michael Hall, Ulrich Neumann, Shuochen Su | The majority of prior monocular depth estimation methods without groundtruth depth guidance focus on driving scenarios. We show that such methods generalize poorly to unseen complex indoor scenes, where objects are cluttered and arbitrarily arranged in the near field. To obtain more robustness, we propose a structure distillation approach to learn knacks from an off-the-shelf relative depth estimator that produces structured but metric-agnostic depth. By combining structure distillation with a branch that learns metrics from left-right consistency, we attain structured and metric depth for generic indoor scenes and make inferences in real-time. To facilitate learning and evaluation, we collect SimSIN, a dataset from simulation with thousands of environments, and UniSIN, a dataset that contains about 500 real scan sequences of generic indoor environments. We experiment in both sim-to-real and real-to-real settings, and show improvements, as well as in downstream applications using our depth maps. This work provides a full study, covering methods, data, and applications aspects. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Toward_Practical_Monocular_Indoor_Depth_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Toward_Practical_Monocular_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02306 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Toward_Practical_Monocular_Indoor_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Toward_Practical_Monocular_Indoor_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Attention Concatenation Volume for Accurate and Efficient Stereo Matching | Gangwei Xu, Junda Cheng, Peng Guo, Xin Yang | Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-of-the-art performance on several benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Attention_Concatenation_Volume_for_Accurate_and_Efficient_Stereo_Matching_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.02146 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Attention_Concatenation_Volume_for_Accurate_and_Efficient_Stereo_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Attention_Concatenation_Volume_for_Accurate_and_Efficient_Stereo_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Distinctive Margin Toward Active Domain Adaptation | Ming Xie, Yuxi Li, Yabiao Wang, Zekun Luo, Zhenye Gan, Zhongyi Sun, Mingmin Chi, Chengjie Wang, Pei Wang | Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data. Nevertheless, most active learning methods are not inherently designed to handle domain gap between data distribution, on the other hand, some active domain adaptation methods (ADA) usually requires complicated query functions, which is vulnerable to overfitting. In this work, we propose a concise but effective ADA method called Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and a margin sampling algorithm for data selection. We provide theoretical analysis to show that SDM works like a Support Vector Machine, storing hard examples around decision boundaries and exploiting them to find informative and transferable data. In addition, we propose two variants of our method, one is designed to adaptively adjust the gradient from margin loss, the other boosts the selectivity of margin sampling by taking the gradient direction into account. We benchmark SDM with standard active learning setting, demonstrating our algorithm achieves competitive results with good data scalability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Learning_Distinctive_Margin_Toward_Active_Domain_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Learning_Distinctive_Margin_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05738 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Learning_Distinctive_Margin_Toward_Active_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Learning_Distinctive_Margin_Toward_Active_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
Zero-Query Transfer Attacks on Context-Aware Object Detectors | Zikui Cai, Shantanu Rane, Alejandro E. Brito, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy-Chowdhury, M. Salman Asif | Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check, wherein, if the detected objects are not consistent with an appropriately defined context, then an attack is suspected. Stronger attacks are needed to fool such context-aware detectors. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check of black-box object detectors operating on complex, natural scenes. Unlike many black-box attacks that perform repeated attempts and open themselves to detection, we assume a "zero-query" setting, where the attacker has no knowledge of the classification decisions of the victim system. First, we derive multiple attack plans that assign incorrect labels to victim objects in a context-consistent manner. Then we design and use a novel data structure that we call the perturbation success probability matrix, which enables us to filter the attack plans and choose the one most likely to succeed. This final attack plan is implemented using a perturbation-bounded adversarial attack algorithm. We compare our zero-query attack against a few-query scheme that repeatedly checks if the victim system is fooled. We also compare against state-of-the-art context-agnostic attacks. Against a context-aware defense, the fooling rate of our zero-query approach is significantly higher than context-agnostic approaches and higher than that achievable with up to three rounds of the few-query scheme. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_Zero-Query_Transfer_Attacks_on_Context-Aware_Object_Detectors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cai_Zero-Query_Transfer_Attacks_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_Zero-Query_Transfer_Attacks_on_Context-Aware_Object_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_Zero-Query_Transfer_Attacks_on_Context-Aware_Object_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Inertial Localization | Sachini Herath, David Caruso, Chen Liu, Yufan Chen, Yasutaka Furukawa | This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements. This is an exciting and unexplored area of indoor localization research, where we present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations. We developed a solution, dubbed neural inertial localization (NILoc) which 1) uses a neural inertial navigation technique to turn inertial sensor history to a sequence of velocity vectors; then 2) employs a transformer-based neural architecture to find the device location from the sequence of velocity estimates. We only use an IMU sensor, which is energy efficient and privacy-preserving compared to WiFi, cameras, and other data sources. Our approach is significantly faster and achieves competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower. We share our code, model and data at https://sachini.github.io/niloc. | https://openaccess.thecvf.com/content/CVPR2022/papers/Herath_Neural_Inertial_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Herath_Neural_Inertial_Localization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15851 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Herath_Neural_Inertial_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Herath_Neural_Inertial_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement | Jiahao Fan, Huabin Liu, Wenjie Yang, John See, Aixin Zhang, Weiyao Lin | With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most existing works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch is detected independently. However, when the image resolution reaches gigapixel-level, they will suffer from a huge time cost for detecting numerous patches. Different from them, we devise a novel patch arrangement framework for fast object detection on gigapixel-level images. Under this framework, a Patch Arrangement Network (PAN) is proposed to accelerate the detection by determining which patches could be packed together into a compact canvas. Specifically, PAN consists of (1) a Patch Filter Module (PFM) (2) a Patch Packing Module (PPM). PFM filters patch candidates by learning to select patches between two granularities. Subsequently, from the remaining patches, PPM determines how to pack these patches together into a smaller number of canvases. Meanwhile, it generates an ideal layout of patches on canvas. These canvases are fed to the detector to get final results. Experiments show that our method could improve the inference speed on gigapixel-level images by 5 times while maintaining great performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.html | CVPR 2022 | null |
Finding Fallen Objects via Asynchronous Audio-Visual Integration | Chuang Gan, Yi Gu, Siyuan Zhou, Jeremy Schwartz, Seth Alter, James Traer, Dan Gutfreund, Joshua B. Tenenbaum, Josh H. McDermott, Antonio Torralba | The way an object looks and sounds provide complementary reflections of its physical properties. In many settings cues from vision and audition arrive asynchronously but must be integrated, as when we hear an object dropped on the floor and then must find it. In this paper, we introduce a setting in which to study multi-modal object localization in 3D virtual environments. An object is dropped somewhere in a room. An embodied robot agent, equipped with a camera and microphone, must determine what object has been dropped -- and where -- by combining audio and visual signals with knowledge of the underlying physics. To study this problem, we have generated a large-scale dataset -- the Fallen Objects dataset -- that includes 8000 instances of 30 physical object categories in 64 rooms. The dataset uses the ThreeDWorld Platform that can simulate physics-based impact sounds and complex physical interactions between objects in a photorealistic setting. As a first step toward addressing this challenge, we develop a set of embodied agent baselines, based on imitation learning, reinforcement learning, and modular planning, and perform an in-depth analysis of the challenge of this new task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gan_Finding_Fallen_Objects_via_Asynchronous_Audio-Visual_Integration_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gan_Finding_Fallen_Objects_via_Asynchronous_Audio-Visual_Integration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gan_Finding_Fallen_Objects_via_Asynchronous_Audio-Visual_Integration_CVPR_2022_paper.html | CVPR 2022 | null |
Learning sRGB-to-Raw-RGB De-Rendering With Content-Aware Metadata | Seonghyeon Nam, Abhijith Punnappurath, Marcus A. Brubaker, Michael S. Brown | Most camera images are rendered and saved in the standard RGB (sRGB) format by the camera's hardware. Due to the in-camera photo-finishing routines, nonlinear sRGB images are undesirable for computer vision tasks that assume a direct relationship between pixel values and scene radiance. For such applications, linear raw-RGB sensor images are preferred. Saving images in their raw-RGB format is still uncommon due to the large storage requirement and lack of support by many imaging applications. Several "raw reconstruction" methods have been proposed that utilize specialized metadata sampled from the raw-RGB image at capture time and embedded in the sRGB image. This metadata is used to parameterize a mapping function to de-render the sRGB image back to its original raw-RGB format when needed. Existing raw reconstruction methods rely on simple sampling strategies and global mapping to perform the de-rendering. This paper shows how to improve the de-rendering results by jointly learning sampling and reconstruction. Our experiments show that our learned sampling can adapt to the image content to produce better raw reconstructions than existing methods. We also describe an online fine-tuning strategy for the reconstruction network to improve results further. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nam_Learning_sRGB-to-Raw-RGB_De-Rendering_With_Content-Aware_Metadata_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nam_Learning_sRGB-to-Raw-RGB_De-Rendering_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nam_Learning_sRGB-to-Raw-RGB_De-Rendering_With_Content-Aware_Metadata_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nam_Learning_sRGB-to-Raw-RGB_De-Rendering_With_Content-Aware_Metadata_CVPR_2022_paper.html | CVPR 2022 | null |
GraftNet: Towards Domain Generalized Stereo Matching With a Broad-Spectrum and Task-Oriented Feature | Biyang Liu, Huimin Yu, Guodong Qi | Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://github.com/SpadeLiu/Graft-PSMNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_GraftNet_Towards_Domain_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00179 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Total Recall in Industrial Anomaly Detection | Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter Gehler | Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically. The best peforming approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose PatchCore, which uses a maximally representative memory bank of nominal patch-features. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization. On the challenging, widely used MVTec AD benchmark PatchCore achieves an image-level anomaly detection AUROC score of up to 99.6%, more than halving the error compared to the next best competitor. We further report competitive results on two additional datasets and also find competitive results in the few samples regime. Code: github.com/amazon-research/patchcore-inspection | https://openaccess.thecvf.com/content/CVPR2022/papers/Roth_Towards_Total_Recall_in_Industrial_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Roth_Towards_Total_Recall_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.08265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Towards_Total_Recall_in_Industrial_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Towards_Total_Recall_in_Industrial_Anomaly_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
DTA: Physical Camouflage Attacks Using Differentiable Transformation Network | Naufal Suryanto, Yongsu Kim, Hyoeun Kang, Harashta Tatimma Larasati, Youngyeo Yun, Thi-Thu-Huong Le, Hunmin Yang, Se-Yoon Oh, Howon Kim | To perform adversarial attacks in the physical world, many studies have proposed adversarial camouflage, a method to hide a target object by applying camouflage patterns on 3D object surfaces. For obtaining optimal physical adversarial camouflage, previous studies have utilized the so-called neural renderer, as it supports differentiability. However, existing neural renderers cannot fully represent various real-world transformations due to a lack of control of scene parameters compared to the legacy photo-realistic renderers. In this paper, we propose the Differentiable Transformation Attack (DTA), a framework for generating a robust physical adversarial pattern on a target object to camouflage it against object detection models with a wide range of transformations. It utilizes our novel Differentiable Transformation Network (DTN), which learns the expected transformation of a rendered object when the texture is changed while preserving the original properties of the target object. Using our attack framework, an adversary can gain both the advantages of the legacy photo-realistic renderers including various physical-world transformations and the benefit of white-box access by offering differentiability. Our experiments show that our camouflaged 3D vehicles can successfully evade state-of-the-art object detection models in the photo-realistic environment (i.e., CARLA on Unreal Engine). Furthermore, our demonstration on a scaled Tesla Model 3 proves the applicability and transferability of our method to the real world. | https://openaccess.thecvf.com/content/CVPR2022/papers/Suryanto_DTA_Physical_Camouflage_Attacks_Using_Differentiable_Transformation_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suryanto_DTA_Physical_Camouflage_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09831 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Suryanto_DTA_Physical_Camouflage_Attacks_Using_Differentiable_Transformation_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Suryanto_DTA_Physical_Camouflage_Attacks_Using_Differentiable_Transformation_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Recognition of Dashed Curves With Gestalt Law of Continuity | Hanyuan Liu, Chengze Li, Xueting Liu, Tien-Tsin Wong | Dashed curve is a frequently used curve form and is widely used in various drawing and illustration applications. While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of continuity in Gestalt psychology, it is extremely difficult for computers to model the Gestalt law of continuity and recognize the dashed curves since high-level semantic understanding is needed for this task. The various appearances and styles of the dashed curves posed on a potentially noisy background further complicate the task. In this paper, we propose an innovative Transformer-based framework to recognize dashed curves based on both high-level features and low-level clues. The framework manages to learn the computational analogy of the Gestalt Law in various domains to locate and extract instances of dashed curves in both raster and vector representations. Qualitative and quantitative evaluations demonstrate the efficiency and robustness of our framework over all existing solutions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Neural_Recognition_of_Dashed_Curves_With_Gestalt_Law_of_Continuity_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Neural_Recognition_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Recognition_of_Dashed_Curves_With_Gestalt_Law_of_Continuity_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Recognition_of_Dashed_Curves_With_Gestalt_Law_of_Continuity_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Object Detection via Multi-Instance Alignment With Global Class Prototypes | Aoxue Li, Peng Yuan, Zhenguo Li | Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD methods individually learn from labeled data and unlabeled data, without considering the relation between them. To make full use of labeled data, we propose a Multi-instance Alignment model which enhances the prediction consistency based on Global Class Prototypes (MA-GCP). Specifically, we impose the consistency between pseudo ground-truths and their high-IoU candidates by minimizing the cross-entropy loss of their class distributions computed based on global class prototypes. These global class prototypes are estimated with the whole labeled dataset via the exponential moving average algorithm. To evaluate the proposed MA-GCP model, we integrate it into the state-of-the-art SSOD framework and experiments on two benchmark datasets demonstrate the effectiveness of our MA-GCP approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Semi-Supervised_Object_Detection_via_Multi-Instance_Alignment_With_Global_Class_Prototypes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Semi-Supervised_Object_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Semi-Supervised_Object_Detection_via_Multi-Instance_Alignment_With_Global_Class_Prototypes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Semi-Supervised_Object_Detection_via_Multi-Instance_Alignment_With_Global_Class_Prototypes_CVPR_2022_paper.html | CVPR 2022 | null |
HODOR: High-Level Object Descriptors for Object Re-Segmentation in Video Learned From Static Images | Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian Leibe | Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. Without any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Athar_HODOR_High-Level_Object_Descriptors_for_Object_Re-Segmentation_in_Video_Learned_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Athar_HODOR_High-Level_Object_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.09131 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Athar_HODOR_High-Level_Object_Descriptors_for_Object_Re-Segmentation_in_Video_Learned_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Athar_HODOR_High-Level_Object_Descriptors_for_Object_Re-Segmentation_in_Video_Learned_CVPR_2022_paper.html | CVPR 2022 | null |
Point Cloud Color Constancy | Xiaoyan Xing, Yanlin Qian, Sibo Feng, Yuhan Dong, Jiří Matas | In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sensor mounted rigidly with the RGB sensor, and form a 6D cloud where each point contains the coordinates and RGB intensities, noted as (x,y,z,r,g,b). PCCC applies the PointNet architecture to the color constancy problem, deriving the illumination vector point-wise and then making a global decision about the global illumination chromaticity. On two popular RGB-D datasets, which we extend with illumination information, as well as on a novel benchmark, PCCC obtains lower error than the state-of-the-art algorithms. Our method is simple and fast, requiring merely 16*16-size input and reaching speed over 500 fps, including the cost of building the point cloud and net inference. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xing_Point_Cloud_Color_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.11280 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.html | CVPR 2022 | null |
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning | Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata | Human-annotated attributes serve as powerful semantic embeddings in zero-shot learning. However, their annotation process is labor-intensive and needs expert supervision. Current unsupervised semantic embeddings, i.e., word embeddings, enable knowledge transfer between classes. However, word embeddings do not always reflect visual similarities and result in inferior zero-shot performance. We propose to discover semantic embeddings containing discriminative visual properties for zero-shot learning, without requiring any human annotation. Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic relatedness. To associate these clusters with previously unseen classes, we use external knowledge, e.g., word embeddings and propose a novel class relation discovery module. Through quantitative and qualitative evaluation, we demonstrate that our model discovers semantic embeddings that model the visual properties of both seen and unseen classes. Furthermore, we demonstrate on three benchmarks that our visually-grounded semantic embeddings further improve performance over word embeddings across various ZSL models by a large margin. Code is available at https://github.com/wenjiaXu/VGSE | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_VGSE_Visually-Grounded_Semantic_Embeddings_for_Zero-Shot_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_VGSE_Visually-Grounded_Semantic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10444 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_VGSE_Visually-Grounded_Semantic_Embeddings_for_Zero-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_VGSE_Visually-Grounded_Semantic_Embeddings_for_Zero-Shot_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Catching Both Gray and Black Swans: Open-Set Supervised Anomaly Detection | Choubo Ding, Guansong Pang, Chunhua Shen | Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screening, etc. These anomaly examples provide valuable knowledge about the application-specific abnormality, enabling significantly improved detection of similar anomalies in some recent models. However, those anomalies seen during training often do not illustrate every possible class of anomaly, rendering these models ineffective in generalizing to unseen anomaly classes. This paper tackles open-set supervised anomaly detection, in which we learn detection models using the anomaly examples with the objective to detect both seen anomalies ('gray swans') and unseen anomalies ('black swans'). We propose a novel approach that learns disentangled representations of abnormalities illustrated by seen anomalies, pseudo anomalies, and latent residual anomalies (i.e., samples that have unusual residuals compared to the normal data in a latent space), with the last two abnormalities designed to detect unseen anomalies. Extensive experiments on nine real-world anomaly detection datasets show superior performance of our model in detecting seen and unseen anomalies under diverse settings. Code and data are available at: https://github.com/choubo/DRA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Catching_Both_Gray_and_Black_Swans_Open-Set_Supervised_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Catching_Both_Gray_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14506 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Catching_Both_Gray_and_Black_Swans_Open-Set_Supervised_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Catching_Both_Gray_and_Black_Swans_Open-Set_Supervised_Anomaly_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
MLSLT: Towards Multilingual Sign Language Translation | Aoxiong Yin, Zhou Zhao, Weike Jin, Meng Zhang, Xingshan Zeng, Xiaofei He | Most of the research to date focuses on bilingual sign language translation (BSLT). However, such models are inefficient in building multilingual sign language translation systems. To solve this problem, we introduce the multilingual sign language translation (MSLT) task. It aims to use a single model to complete the translation between multiple sign languages and spoken languages. Then, we propose MLSLT, the first MSLT model, which contains two novel dynamic routing mechanisms for controlling the degree of parameter sharing between different languages. Intra-layer language-specific routing controls the proportion of data flowing through shared parameters and language-specific parameters from the token level through a soft gate within the layer, and inter-layer language-specific routing controls and learns the data flow path of different languages at the language level through a soft gate between layers. In order to evaluate the performance of MLSLT, we collect the first publicly available multilingual sign language understanding dataset, Spreadthesign-Ten (SP-10), which contains up to 100 language pairs, e.g., CSL->en, GSG->zh. Experimental results show that the average performance of MLSLT outperforms the baseline MSLT model and the combination of multiple BSLT models in many cases. In addition, we also explore zero-shot translation in sign language and find that our model can achieve comparable performance to the supervised BSLT model on some language pairs. Dataset and more details are at https://mlslt.github.io/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yin_MLSLT_Towards_Multilingual_Sign_Language_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yin_MLSLT_Towards_Multilingual_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_MLSLT_Towards_Multilingual_Sign_Language_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_MLSLT_Towards_Multilingual_Sign_Language_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Towards an End-to-End Framework for Flow-Guided Video Inpainting | Zhen Li, Cheng-Ze Lu, Jianhua Qin, Chun-Le Guo, Ming-Ming Cheng | Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, they are less efficient and rely heavily on the intermediate results from earlier stages. In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules. The three modules correspond with the three stages of previous flow-based methods but can be jointly optimized, leading to a more efficient and effective inpainting process. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods both qualitatively and quantitatively and shows promising efficiency. The code is available at https://github.com/MCG-NKU/E2FGVI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Towards_an_End-to-End_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02663 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Test-Time Adaptation | Dian Chen, Dequan Wang, Trevor Darrell, Sayna Ebrahimi | Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source data. We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseudo labeling scheme with refinement that significantly denoises pseudo labels. The contrastive learning task is applied jointly with pseudo labeling, contrasting positive and negative pairs constructed similarly as MoCo but with source-initialized encoder, and excluding same-class negative pairs indicated by pseudo labels. Meanwhile, we produce pseudo labels online and refine them via soft voting among their nearest neighbors in the target feature space, enabled by maintaining a memory queue. Our method, AdaContrast, achieves state-of-the-art performance on major benchmarks while having several desirable properties compared to existing works, including memory efficiency, insensitivity to hyper-parameters, and better model calibration. Code is released at https://github.com/DianCh/AdaContrast. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Contrastive_Test-Time_Adaptation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.10377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Contrastive_Test-Time_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Contrastive_Test-Time_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
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