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Quality-Aware Pre-Trained Models for Blind Image Quality Assessment | Kai Zhao, Kun Yuan, Ming Sun, Mading Li, Xing Wen | Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years. However, the paucity of labeled data somewhat restrains deep learning-based BIQA methods from unleashing their full potential. In this paper, we propose to solve the problem by a pretext task customized for BIQA in a self-supervised learning manner, which enables learning representations from orders of magnitude more data. To constrain the learning process, we propose a quality-aware contrastive loss based on a simple assumption: the quality of patches from a distorted image should be similar, but vary from patches from the same image with different degradations and patches from different images. Further, we improve the existing degradation process and form a degradation space with the size of roughly 2x10^7. After pre-trained on ImageNet using our method, models are more sensitive to image quality and perform significantly better on downstream BIQA tasks. Experimental results show that our method obtains remarkable improvements on popular BIQA datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.00521 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.html | CVPR 2023 | null |
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology | Shahira Abousamra, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras, Joel Saltz, Chao Chen | In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification. | https://openaccess.thecvf.com/content/CVPR2023/papers/Abousamra_Topology-Guided_Multi-Class_Cell_Context_Generation_for_Digital_Pathology_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.02255 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Abousamra_Topology-Guided_Multi-Class_Cell_Context_Generation_for_Digital_Pathology_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Abousamra_Topology-Guided_Multi-Class_Cell_Context_Generation_for_Digital_Pathology_CVPR_2023_paper.html | CVPR 2023 | null |
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection | Yingjie Wang, Jiajun Deng, Yao Li, Jinshui Hu, Cong Liu, Yu Zhang, Jianmin Ji, Wanli Ouyang, Yanyong Zhang | LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocity hints. Though seemingly natural, how to efficiently combine them for improved feature representation is still unclear. The main challenge arises from that Radar data are extremely sparse and lack height information. Therefore, directly integrating Radar features into LiDAR-centric detection networks is not optimal. In this work, we introduce a bi-directional LiDAR-Radar fusion framework, termed Bi-LRFusion, to tackle the challenges and improve 3D detection for dynamic objects. Technically, Bi-LRFusion involves two steps: first, it enriches Radar's local features by learning important details from the LiDAR branch to alleviate the problems caused by the absence of height information and extreme sparsity; second, it combines LiDAR features with the enhanced Radar features in a unified bird's-eye-view representation. We conduct extensive experiments on nuScenes and ORR datasets, and show that our Bi-LRFusion achieves state-of-the-art performance for detecting dynamic objects. Notably, Radar data in these two datasets have different formats, which demonstrates the generalizability of our method. Codes will be published. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_Fusion_for_3D_Dynamic_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_Fusion_for_3D_Dynamic_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Bi-LRFusion_Bi-Directional_LiDAR-Radar_Fusion_for_3D_Dynamic_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Graph Convolutional Subspace Clustering | Lai Wei, Zhengwei Chen, Jun Yin, Changming Zhu, Rigui Zhou, Jin Liu | Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications. The existing spectral-type subspace clustering algorithms either focus on designing constraints for the reconstruction coefficient matrix or feature extraction methods for finding latent features of original data samples. In this paper, inspired by graph convolutional networks, we use the graph convolution technique to develop a feature extraction method and a coefficient matrix constraint simultaneously. And the graph-convolutional operator is updated iteratively and adaptively in our proposed algorithm. Hence, we call the proposed method adaptive graph convolutional subspace clustering (AGCSC). We claim that, by using AGCSC, the aggregated feature representation of original data samples is suitable for subspace clustering, and the coefficient matrix could reveal the subspace structure of the original data set more faithfully. Finally, plenty of subspace clustering experiments prove our conclusions and show that AGCSC outperforms some related methods as well as some deep models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Adaptive_Graph_Convolutional_Subspace_Clustering_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.03414 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Adaptive_Graph_Convolutional_Subspace_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Adaptive_Graph_Convolutional_Subspace_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding | Gen Li, Varun Jampani, Deqing Sun, Laura Sevilla-Lara | Humans excel at acquiring knowledge through observation. For example, we can learn to use new tools by watching demonstrations. This skill is fundamental for intelligent systems to interact with the world. A key step to acquire this skill is to identify what part of the object affords each action, which is called affordance grounding. In this paper, we address this problem and propose a framework called LOCATE that can identify matching object parts across images, to transfer knowledge from images where an object is being used (exocentric images used for learning), to images where the object is inactive (egocentric ones used to test). To this end, we first find interaction areas and extract their feature embeddings. Then we learn to aggregate the embeddings into compact prototypes (human, object part, and background), and select the one representing the object part. Finally, we use the selected prototype to guide affordance grounding. We do this in a weakly supervised manner, learning only from image-level affordance and object labels. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods by a large margin on both seen and unseen objects. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_LOCATE_Localize_and_Transfer_Object_Parts_for_Weakly_Supervised_Affordance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_LOCATE_Localize_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09665 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_LOCATE_Localize_and_Transfer_Object_Parts_for_Weakly_Supervised_Affordance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_LOCATE_Localize_and_Transfer_Object_Parts_for_Weakly_Supervised_Affordance_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Steerable Function for Efficient Image Resampling | Jiacheng Li, Chang Chen, Wei Huang, Zhiqiang Lang, Fenglong Song, Youliang Yan, Zhiwei Xiong | Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x2 upsampling on Manga109. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Learning_Steerable_Function_for_Efficient_Image_Resampling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Learning_Steerable_Function_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Steerable_Function_for_Efficient_Image_Resampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Steerable_Function_for_Efficient_Image_Resampling_CVPR_2023_paper.html | CVPR 2023 | null |
TokenHPE: Learning Orientation Tokens for Efficient Head Pose Estimation via Transformers | Cheng Zhang, Hai Liu, Yongjian Deng, Bochen Xie, Youfu Li | Head pose estimation (HPE) has been widely used in the fields of human machine interaction, self-driving, and attention estimation. However, existing methods cannot deal with extreme head pose randomness and serious occlusions. To address these challenges, we identify three cues from head images, namely, neighborhood similarities, significant facial changes, and critical minority relationships. To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned. Specifically, we design several orientation tokens to explicitly encode the basic orientation regions. Meanwhile, a novel token guide multi-loss function is designed to guide the orientation tokens as they learn the desired regional similarities and relationships. We evaluate the proposed method on three challenging benchmark HPE datasets. Experiments show that our method achieves better performance compared with state-of-the-art methods. Our code is publicly available at https://github.com/zc2023/TokenHPE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_TokenHPE_Learning_Orientation_Tokens_for_Efficient_Head_Pose_Estimation_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_TokenHPE_Learning_Orientation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_TokenHPE_Learning_Orientation_Tokens_for_Efficient_Head_Pose_Estimation_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_TokenHPE_Learning_Orientation_Tokens_for_Efficient_Head_Pose_Estimation_via_CVPR_2023_paper.html | CVPR 2023 | null |
BioNet: A Biologically-Inspired Network for Face Recognition | Pengyu Li | Recently, whether and how cutting-edge Neuroscience findings can inspire Artificial Intelligence (AI) confuse both communities and draw much discussion. As one of the most critical fields in AI, Computer Vision (CV) also pays much attention to the discussion. To show our ideas and experimental evidence to the discussion, we focus on one of the most broadly researched topics both in Neuroscience and CV fields, i.e., Face Recognition (FR). Neuroscience studies show that face attributes are essential to the human face-recognizing system. How the attributes contribute also be explained by the Neuroscience community. Even though a few CV works improved the FR performance with attribute enhancement, none of them are inspired by the human face-recognizing mechanism nor boosted performance significantly. To show our idea experimentally, we model the biological characteristics of the human face-recognizing system with classical Convolutional Neural Network Operators (CNN Ops) purposely. We name the proposed Biologically-inspired Network as BioNet. Our BioNet consists of two cascade sub-networks, i.e., the Visual Cortex Network (VCN) and the Inferotemporal Cortex Network (ICN). The VCN is modeled with a classical CNN backbone. The proposed ICN comprises three biologically-inspired modules, i.e., the Cortex Functional Compartmentalization, the Compartment Response Transform, and the Response Intensity Modulation. The experiments prove that: 1) The cutting-edge findings about the human face-recognizing system can further boost the CNN-based FR network. 2) With the biological mechanism, both identity-related attributes (e.g., gender) and identity-unrelated attributes (e.g., expression) can benefit the deep FR models. Surprisingly, the identity-unrelated ones contribute even more than the identity-related ones. 3) The proposed BioNet significantly boosts state-of-the-art on standard FR benchmark datasets. For example, BioNet boosts IJB-B@1e-6 from 52.12% to 68.28% and MegaFace from 98.74% to 99.19%. The source code will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_BioNet_A_Biologically-Inspired_Network_for_Face_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_BioNet_A_Biologically-Inspired_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_BioNet_A_Biologically-Inspired_Network_for_Face_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_BioNet_A_Biologically-Inspired_Network_for_Face_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Scaling Up GANs for Text-to-Image Synthesis | Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, Taesung Park | The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that naively increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel images in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Scaling_Up_GANs_for_Text-to-Image_Synthesis_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Scaling_Up_GANs_for_Text-to-Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Scaling_Up_GANs_for_Text-to-Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
DepGraph: Towards Any Structural Pruning | Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, Xinchao Wang | Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks. However, the parameter-grouping patterns vary widely across different models, making architecture-specific pruners, which rely on manually-designed grouping schemes, non-generalizable to new architectures. In this work, we study a highly-challenging yet barely-explored task, any structural pruning, to tackle general structural pruning of arbitrary architecture like CNNs, RNNs, GNNs and Transformers. The most prominent obstacle towards this goal lies in the structural coupling, which not only forces different layers to be pruned simultaneously, but also expects all removed parameters to be consistently unimportant, thereby avoiding structural issues and significant performance degradation after pruning. To address this problem, we propose a general and fully automatic method, Dependency Graph (DepGraph), to explicitly model the dependency between layers and comprehensively group coupled parameters for pruning. In this work, we extensively evaluate our method on several architectures and tasks, including ResNe(X)t, DenseNet, MobileNet and Vision transformer for images, GAT for graph, DGCNN for 3D point cloud, alongside LSTM for language, and demonstrate that, even with a simple norm-based criterion, the proposed method consistently yields gratifying performances. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.12900 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring Discontinuity for Video Frame Interpolation | Sangjin Lee, Hyeongmin Lee, Chajin Shin, Hanbin Son, Sangyoun Lee | Video frame interpolation (VFI) is the task that synthesizes the intermediate frame given two consecutive frames. Most of the previous studies have focused on appropriate frame warping operations and refinement modules for the warped frames. These studies have been conducted on natural videos containing only continuous motions. However, many practical videos contain various unnatural objects with discontinuous motions such as logos, user interfaces and subtitles. We propose three techniques that can make the existing deep learning-based VFI architectures robust to these elements. First is a novel data augmentation strategy called figure-text mixing (FTM) which can make the models learn discontinuous motions during training stage without any extra dataset. Second, we propose a simple but effective module that predicts a map called discontinuity map (D-map), which densely distinguishes between areas of continuous and discontinuous motions. Lastly, we propose loss functions to give supervisions of the discontinuous motion areas which can be applied along with FTM and D-map. We additionally collect a special test benchmark called Graphical Discontinuous Motion (GDM) dataset consisting of some mobile games and chatting videos. Applied to the various state-of-the-art VFI networks, our method significantly improves the interpolation qualities on the videos from not only GDM dataset, but also the existing benchmarks containing only continuous motions such as Vimeo90K, UCF101, and DAVIS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Exploring_Discontinuity_for_Video_Frame_Interpolation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Exploring_Discontinuity_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2202.07291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Exploring_Discontinuity_for_Video_Frame_Interpolation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Exploring_Discontinuity_for_Video_Frame_Interpolation_CVPR_2023_paper.html | CVPR 2023 | null |
DynamicStereo: Consistent Dynamic Depth From Stereo Videos | Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht | We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consistency is especially important for immersive AR or VR scenarios, where flickering greatly diminishes the user experience. We propose DynamicStereo, a novel transformer-based architecture to estimate disparity for stereo videos. The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions. Our architecture is designed to process stereo videos efficiently through divided attention layers. We also introduce Dynamic Replica, a new benchmark dataset containing synthetic videos of people and animals in scanned environments, which provides complementary training and evaluation data for dynamic stereo closer to real applications than existing datasets. Training with this dataset further improves the quality of predictions of our proposed DynamicStereo as well as prior methods. Finally, it acts as a benchmark for consistent stereo methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Karaev_DynamicStereo_Consistent_Dynamic_Depth_From_Stereo_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karaev_DynamicStereo_Consistent_Dynamic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02296 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karaev_DynamicStereo_Consistent_Dynamic_Depth_From_Stereo_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karaev_DynamicStereo_Consistent_Dynamic_Depth_From_Stereo_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Cut and Learn for Unsupervised Object Detection and Instance Segmentation | Xudong Wang, Rohit Girdhar, Stella X. Yu, Ishan Misra | We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'discover' objects without supervision and amplify it to train a state-of-the-art localization model without any human labels. CutLER first uses our proposed MaskCut approach to generate coarse masks for multiple objects in an image, and then learns a detector on these masks using our robust loss function. We further improve performance by self-training the model on its predictions. Compared to prior work, CutLER is simpler, compatible with different detection architectures, and detects multiple objects. CutLER is also a zero-shot unsupervised detector and improves detection performance AP_50 by over 2.7x on 11 benchmarks across domains like video frames, paintings, sketches, etc. With finetuning, CutLER serves as a low-shot detector surpassing MoCo-v2 by 7.3% AP^box and 6.6% AP^mask on COCO when training with 5% labels. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Cut_and_Learn_for_Unsupervised_Object_Detection_and_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Cut_and_Learn_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.11320 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Cut_and_Learn_for_Unsupervised_Object_Detection_and_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Cut_and_Learn_for_Unsupervised_Object_Detection_and_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Privacy-Preserving Adversarial Facial Features | Zhibo Wang, He Wang, Shuaifan Jin, Wenwen Zhang, Jiahui Hu, Yan Wang, Peng Sun, Wei Yuan, Kaixin Liu, Kui Ren | Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for real-time recognition. However, such features can still be exploited to recover the appearance of the original face by building a reconstruction network. Although several privacy-preserving methods have been proposed, the enhancement of face privacy protection is at the expense of accuracy degradation. In this paper, we propose an adversarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks. To this end, we design a shadow model which simulates the attackers' behavior to capture the mapping function from facial features to images and generate adversarial latent noise to disrupt the mapping. The adversarial features rather than the original features are stored in the server's database to prevent leaked features from exposing facial information. Moreover, the AdvFace requires no changes to the face recognition network and can be implemented as a privacy-enhancing plugin in deployed face recognition systems. Extensive experimental results demonstrate that AdvFace outperforms the state-of-the-art face privacy-preserving methods in defending against reconstruction attacks while maintaining face recognition accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Privacy-Preserving_Adversarial_Facial_Features_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Privacy-Preserving_Adversarial_Facial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.05391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Privacy-Preserving_Adversarial_Facial_Features_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Privacy-Preserving_Adversarial_Facial_Features_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring the Relationship Between Architectural Design and Adversarially Robust Generalization | Aishan Liu, Shiyu Tang, Siyuan Liang, Ruihao Gong, Boxi Wu, Xianglong Liu, Dacheng Tao | Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization gap on unseen testing adversaries, deemed as the adversarially robust generalization problem. Despite the preliminary understandings devoted to adversarially robust generalization, little is known from the architectural perspective. To bridge the gap, this paper for the first time systematically investigated the relationship between adversarially robust generalization and architectural design. In particular, we comprehensively evaluated 20 most representative adversarially trained architectures on ImageNette and CIFAR-10 datasets towards multiple l_p-norm adversarial attacks. Based on the extensive experiments, we found that, under aligned settings, Vision Transformers (e.g., PVT, CoAtNet) often yield better adversarially robust generalization while CNNs tend to overfit on specific attacks and fail to generalize on multiple adversaries. To better understand the nature behind it, we conduct theoretical analysis via the lens of Rademacher complexity. We revealed the fact that the higher weight sparsity contributes significantly towards the better adversarially robust generalization of Transformers, which can be often achieved by the specially-designed attention blocks. We hope our paper could help to better understand the mechanism for designing robust DNNs. Our model weights can be found at http://robust.art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Exploring_the_Relationship_Between_Architectural_Design_and_Adversarially_Robust_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Exploring_the_Relationship_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Exploring_the_Relationship_Between_Architectural_Design_and_Adversarially_Robust_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Exploring_the_Relationship_Between_Architectural_Design_and_Adversarially_Robust_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Vid2Avatar: 3D Avatar Reconstruction From Videos in the Wild via Self-Supervised Scene Decomposition | Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, Otmar Hilliges | We present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos is difficult. Solving it requires accurately separating humans from arbitrary backgrounds. Moreover, it requires reconstructing detailed 3D surface from short video sequences, making it even more challenging. Despite these challenges, our method does not require any groundtruth supervision or priors extracted from large datasets of clothed human scans, nor do we rely on any external segmentation modules. Instead, it solves the tasks of scene decomposition and surface reconstruction directly in 3D by modeling both the human and the background in the scene jointly, parameterized via two separate neural fields. Specifically, we define a temporally consistent human representation in canonical space and formulate a global optimization over the background model, the canonical human shape and texture, and per-frame human pose parameters. A coarse-to-fine sampling strategy for volume rendering and novel objectives are introduced for a clean separation of dynamic human and static background, yielding detailed and robust 3D human reconstructions. The evaluation of our method shows improvements over prior art on publicly available datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Vid2Avatar_3D_Avatar_Reconstruction_From_Videos_in_the_Wild_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Vid2Avatar_3D_Avatar_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.11566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Vid2Avatar_3D_Avatar_Reconstruction_From_Videos_in_the_Wild_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Vid2Avatar_3D_Avatar_Reconstruction_From_Videos_in_the_Wild_via_CVPR_2023_paper.html | CVPR 2023 | null |
Task Residual for Tuning Vision-Language Models | Tao Yu, Zhihe Lu, Xin Jin, Zhibo Chen, Xinchao Wang | Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://github.com/geekyutao/TaskRes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Task_Residual_for_Tuning_Vision-Language_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Task_Residual_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10277 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Task_Residual_for_Tuning_Vision-Language_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Task_Residual_for_Tuning_Vision-Language_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Side Adapter Network for Open-Vocabulary Semantic Segmentation | Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, Xiang Bai | This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named SAN. Our approach models the semantic segmentation task as a region recognition problem. A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks. This decoupled design has the benefit CLIP in recognizing the class of mask proposals. Since the attached side network can reuse CLIP features, it can be very light. In addition, the entire network can be trained end-to-end, allowing the side network to be adapted to the frozen CLIP model, which makes the predicted mask proposals CLIP-aware. Our approach is fast, accurate, and only adds a few additional trainable parameters. We evaluate our approach on multiple semantic segmentation benchmarks. Our method significantly outperforms other counterparts, with up to 18 times fewer trainable parameters and 19 times faster inference speed. We hope our approach will serve as a solid baseline and help ease future research in open-vocabulary semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Side_Adapter_Network_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Side_Adapter_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.12242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Side_Adapter_Network_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Side_Adapter_Network_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Network Expansion for Practical Training Acceleration | Ning Ding, Yehui Tang, Kai Han, Chao Xu, Yunhe Wang | Recently, the sizes of deep neural networks and training datasets both increase drastically to pursue better performance in a practical sense. With the prevalence of transformer-based models in vision tasks, even more pressure is laid on the GPU platforms to train these heavy models, which consumes a large amount of time and computing resources as well. Therefore, it's crucial to accelerate the training process of deep neural networks. In this paper, we propose a general network expansion method to reduce the practical time cost of the model training process. Specifically, we utilize both width- and depth-level sparsity of dense models to accelerate the training of deep neural networks. Firstly, we pick a sparse sub-network from the original dense model by reducing the number of parameters as the starting point of training. Then the sparse architecture will gradually expand during the training procedure and finally grow into a dense one. We design different expanding strategies to grow CNNs and ViTs respectively, due to the great heterogeneity in between the two architectures. Our method can be easily integrated into popular deep learning frameworks, which saves considerable training time and hardware resources. Extensive experiments show that our acceleration method can significantly speed up the training process of modern vision models on general GPU devices with negligible performance drop (e.g. 1.42x faster for ResNet-101 and 1.34x faster for DeiT-base on ImageNet-1k). The code is available at https://github.com/huawei-noah/Efficient-Computing/tree/master/TrainingAcceleration/NetworkExpansion and https://gitee.com/mindspore/hub/blob/master/mshub_res/assets/noah-cvlab/gpu/1.8/networkexpansion_v1.0_imagenet2012.md. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Network_Expansion_for_Practical_Training_Acceleration_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Network_Expansion_for_Practical_Training_Acceleration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Network_Expansion_for_Practical_Training_Acceleration_CVPR_2023_paper.html | CVPR 2023 | null |
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition | Jian Li, Ziyao Meng, Daqian Shi, Rui Song, Xiaolei Diao, Jingwen Wang, Hao Xu | Deep Neural Networks (DNNs) are rather restrictive in long-tailed data, since they commonly exhibit an under-representation for minority classes. Various remedies have been proposed to tackle this problem from different perspectives, but they ignore the impact of the density of Backbone Features (BFs) on this issue. Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters. In practical applications, these features are discretely mapped or even cross the decision boundary resulting in misclassification. Inspired by this observation, we propose a simple and generic method, namely Feature Clusters Compression (FCC), to increase the density of BFs by compressing backbone feature clusters. The proposed FCC can be easily achieved by only multiplying original BFs by a scaling factor in training phase, which establishes a linear compression relationship between the original and multiplied features, and forces DNNs to map the former into denser clusters. In test phase, we directly feed original features without multiplying the factor to the classifier, such that BFs of test samples are mapped closer together and do not easily cross the decision boundary. Meanwhile, FCC can be friendly combined with existing long-tailed methods and further boost them. We apply FCC to numerous state-of-the-art methods and evaluate them on widely used long-tailed benchmark datasets. Extensive experiments fully verify the effectiveness and generality of our method. Code is available at https://github.com/lijian16/FCC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_FCC_Feature_Clusters_Compression_for_Long-Tailed_Visual_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_FCC_Feature_Clusters_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_FCC_Feature_Clusters_Compression_for_Long-Tailed_Visual_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_FCC_Feature_Clusters_Compression_for_Long-Tailed_Visual_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition | Hanyang Wang, Bo Li, Shuang Wu, Siyuan Shen, Feng Liu, Shouhong Ding, Aimin Zhou | Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format. Previous research has considered non-target frames as noisy frames, but we propose that it should be treated as a weakly supervised problem. We also identify the imbalance of short- and long-term temporal relationships in DFER. Therefore, we introduce the Multi-3D Dynamic Facial Expression Learning (M3DFEL) framework, which utilizes Multi-Instance Learning (MIL) to handle inexact labels. M3DFEL generates 3D-instances to model the strong short-term temporal relationship and utilizes 3DCNNs for feature extraction. The Dynamic Long-term Instance Aggregation Module (DLIAM) is then utilized to learn the long-term temporal relationships and dynamically aggregate the instances. Our experiments on DFEW and FERV39K datasets show that M3DFEL outperforms existing state-of-the-art approaches with a vanilla R3D18 backbone. The source code is available at https://github.com/faceeyes/M3DFEL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Rethinking_the_Learning_Paradigm_for_Dynamic_Facial_Expression_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Rethinking_the_Learning_Paradigm_for_Dynamic_Facial_Expression_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Rethinking_the_Learning_Paradigm_for_Dynamic_Facial_Expression_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference | Tenghao Cai, Zhizhong Zhang, Xin Tan, Yanyun Qu, Guannan Jiang, Chengjie Wang, Yuan Xie | Incremental learning could be roughly divided into two categories, i.e., class- and task-incremental learning. The main difference is whether the task ID is given during evaluation. In this paper, we show this task information is indeed a strong prior knowledge, which will bring significant improvement over class-incremental learning baseline, e.g., DER. Based on this observation, we propose a gate network to predict the task ID for class incremental inference. This is challenging as there is no explicit semantic relationship between categories in the concept of task. Therefore, we propose a multi-centroid task descriptor by assuming the data within a task can form multiple clusters. The cluster centers are optimized by pulling relevant sample-centroid pairs while pushing others away, which ensures that there is at least one centroid close to a given sample. To select relevant pairs, we use class prototypes as proxies and solve a bipartite matching problem, making the task descriptor representative yet not degenerate to uni-modal. As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks. Extensive experiments show our approach achieves state-of-the-art results, e.g., we achieve 72.41% average accuracy on CIFAR100-B0S50, outperforming DER by 3.40%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_Multi-Centroid_Task_Descriptor_for_Dynamic_Class_Incremental_Inference_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_Multi-Centroid_Task_Descriptor_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Multi-Centroid_Task_Descriptor_for_Dynamic_Class_Incremental_Inference_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Multi-Centroid_Task_Descriptor_for_Dynamic_Class_Incremental_Inference_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Prompt Learning for Multi-Task Learning | Yajing Liu, Yuning Lu, Hao Liu, Yaozu An, Zhuoran Xu, Zhuokun Yao, Baofeng Zhang, Zhiwei Xiong, Chenguang Gui | Vision-language models (VLMs) can effectively transfer to various vision tasks via prompt learning. Real-world scenarios often require adapting a model to multiple similar yet distinct tasks. Existing methods focus on learning a specific prompt for each task, limiting the ability to exploit potentially shared information from other tasks. Naively training a task-shared prompt using a combination of all tasks ignores fine-grained task correlations. Significant discrepancies across tasks could cause negative transferring. Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks. Our method quantifies inter-task affinity and subsequently constructs a hierarchical task tree. Task-shared prompts learned by internal nodes explore the information within the corresponding task group, while task-individual prompts learned by leaf nodes obtain fine-grained information targeted at each task. The combination of hierarchical prompts provides high-quality content of different granularity. We evaluate HiPro on four multi-task learning datasets. The results demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Hierarchical_Prompt_Learning_for_Multi-Task_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Hierarchical_Prompt_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Hierarchical_Prompt_Learning_for_Multi-Task_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Hierarchical_Prompt_Learning_for_Multi-Task_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography | Yue Cao, Ming Liu, Shuai Liu, Xiaotao Wang, Lei Lei, Wangmeng Zuo | Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling. To tap the potential of learning-based sensor noise modeling, we investigate the noise formation in a typical imaging process and propose a novel physics-guided ISO-dependent sensor noise modeling approach. Specifically, we build a normalizing flow-based framework to represent the complex noise characteristics of CMOS camera sensors. Each component of the noise model is dedicated to a particular kind of noise under the guidance of physical models. Moreover, we take into consideration of the ISO dependence in the noise model, which is not completely considered by the existing learning-based methods. For training the proposed noise model, a new dataset is further collected with paired noisy-clean images, as well as flat-field and bias frames covering a wide range of ISO settings. Compared to existing methods, the proposed noise model benefits from the flexible structure and accurate modeling capabilities, which can help achieve better denoising performance in extreme low-light scenes. The source code and collected dataset will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Physics-Guided_ISO-Dependent_Sensor_Noise_Modeling_for_Extreme_Low-Light_Photography_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Physics-Guided_ISO-Dependent_Sensor_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Physics-Guided_ISO-Dependent_Sensor_Noise_Modeling_for_Extreme_Low-Light_Photography_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Physics-Guided_ISO-Dependent_Sensor_Noise_Modeling_for_Extreme_Low-Light_Photography_CVPR_2023_paper.html | CVPR 2023 | null |
RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer | Jiahao Wang, Songyang Zhang, Yong Liu, Taiqiang Wu, Yujiu Yang, Xihui Liu, Kai Chen, Ping Luo, Dahua Lin | This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_RIFormer_Keep_Your_Vision_Backbone_Effective_but_Removing_Token_Mixer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_RIFormer_Keep_Your_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_RIFormer_Keep_Your_Vision_Backbone_Effective_but_Removing_Token_Mixer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_RIFormer_Keep_Your_Vision_Backbone_Effective_but_Removing_Token_Mixer_CVPR_2023_paper.html | CVPR 2023 | null |
Context-Based Trit-Plane Coding for Progressive Image Compression | Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim | Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately and thus encode the trit-planes compactly. Second, we develop the context-based distortion reduction module to refine partial latent tensors from the trit-planes and improve the reconstructed image quality. Third, we propose a retraining scheme for the decoder to attain better rate-distortion tradeoffs. Extensive experiments show that CTC outperforms the baseline trit-plane codec significantly, e.g. by -14.84% in BD-rate on the Kodak lossless dataset, while increasing the time complexity only marginally. The source codes are available at https://github.com/seungminjeon-github/CTC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeon_Context-Based_Trit-Plane_Coding_for_Progressive_Image_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeon_Context-Based_Trit-Plane_Coding_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.05715 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeon_Context-Based_Trit-Plane_Coding_for_Progressive_Image_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeon_Context-Based_Trit-Plane_Coding_for_Progressive_Image_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching | Dongliang Cao, Florian Bernard | The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common representation of raw real-world 3D data (e.g. from laser scanners), meshes encode rich and expressive topological information, but their creation typically requires some form of (often manual) curation. In turn, methods that purely rely on point clouds are unable to meet the matching quality of mesh-based methods that utilise the additional topological structure. In this work we close this gap by introducing a self-supervised multimodal learning strategy that combines mesh-based functional map regularisation with a contrastive loss that couples mesh and point cloud data. Our shape matching approach allows to obtain intramodal correspondences for triangle meshes, complete point clouds, and partially observed point clouds, as well as correspondences across these data modalities. We demonstrate that our method achieves state-of-the-art results on several challenging benchmark datasets even in comparison to recent supervised methods, and that our method reaches previously unseen cross-dataset generalisation ability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Self-Supervised_Learning_for_Multimodal_Non-Rigid_3D_Shape_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Self-Supervised_Learning_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10971 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Self-Supervised_Learning_for_Multimodal_Non-Rigid_3D_Shape_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Self-Supervised_Learning_for_Multimodal_Non-Rigid_3D_Shape_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Recurrent Vision Transformers for Object Detection With Event Cameras | Mathias Gehrig, Davide Scaramuzza | We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: First, a convolutional prior that can be regarded as a conditional positional embedding. Second, local- and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (<12 ms on a T4 GPU) and favorable parameter efficiency (5 times fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gehrig_Recurrent_Vision_Transformers_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05598 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.html | CVPR 2023 | null |
Ham2Pose: Animating Sign Language Notation Into Pose Sequences | Rotem Shalev Arkushin, Amit Moryossef, Ohad Fried | Translating spoken languages into Sign languages is necessary for open communication between the hearing and hearing-impaired communities. To achieve this goal, we propose the first method for animating a text written in HamNoSys, a lexical Sign language notation, into signed pose sequences. As HamNoSys is universal by design, our proposed method offers a generic solution invariant to the target Sign language. Our method gradually generates pose predictions using transformer encoders that create meaningful representations of the text and poses while considering their spatial and temporal information. We use weak supervision for the training process and show that our method succeeds in learning from partial and inaccurate data. Additionally, we offer a new distance measurement that considers missing keypoints, to measure the distance between pose sequences using DTW-MJE. We validate its correctness using AUTSL, a large-scale Sign language dataset, show that it measures the distance between pose sequences more accurately than existing measurements, and use it to assess the quality of our generated pose sequences. Code for the data pre-processing, the model, and the distance measurement is publicly released for future research. | https://openaccess.thecvf.com/content/CVPR2023/papers/Arkushin_Ham2Pose_Animating_Sign_Language_Notation_Into_Pose_Sequences_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Arkushin_Ham2Pose_Animating_Sign_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Arkushin_Ham2Pose_Animating_Sign_Language_Notation_Into_Pose_Sequences_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Arkushin_Ham2Pose_Animating_Sign_Language_Notation_Into_Pose_Sequences_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Set Likelihood Maximization for Few-Shot Learning | Malik Boudiaf, Etienne Bennequin, Myriam Tami, Antoine Toubhans, Pablo Piantanida, Celine Hudelot, Ismail Ben Ayed | We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query instances at inference. Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. Our formulation embeds supervision constraints from the support set and additional penalties discouraging overconfident predictions on the query set. We proceed with a block-coordinate descent, with the latent scores and parametric model co-optimized alternately, thereby benefiting from each other. We call our resulting formulation Open-Set Likelihood Optimization (OSLO). OSLO is interpretable and fully modular; it can be applied on top of any pre-trained model seamlessly. Through extensive experiments, we show that our method surpasses existing inductive and transductive methods on both aspects of open-set recognition, namely inlier classification and outlier detection. Code is available at https://github.com/ebennequin/few-shot-open-set. | https://openaccess.thecvf.com/content/CVPR2023/papers/Boudiaf_Open-Set_Likelihood_Maximization_for_Few-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Boudiaf_Open-Set_Likelihood_Maximization_for_Few-Shot_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.08390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Boudiaf_Open-Set_Likelihood_Maximization_for_Few-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Boudiaf_Open-Set_Likelihood_Maximization_for_Few-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection | Jiawei Ma, Yulei Niu, Jincheng Xu, Shiyuan Huang, Guangxing Han, Shih-Fu Chang | Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approaches enhance few-shot generalization with the sacrifice of base-class performance, or maintain high precision in base-class detection with limited improvement in novel-class adaptation. In this paper, we point out the reason is insufficient Discriminative feature learning for all of the classes. As such, we propose a new training framework, DiGeo, to learn Geometry-aware features of inter-class separation and intra-class compactness. To guide the separation of feature clusters, we derive an offline simplex equiangular tight frame (ETF) classifier whose weights serve as class centers and are maximally and equally separated. To tighten the cluster for each class, we include adaptive class-specific margins into the classification loss and encourage the features close to the class centers. Experimental studies on two few-shot benchmark datasets (PASCAL VOC, MSCOCO) and one long-tail dataset (LVIS) demonstrate that, with a single model, our method can effectively improve generalization on novel classes without hurting the detection of base classes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_DiGeo_Discriminative_Geometry-Aware_Learning_for_Generalized_Few-Shot_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_DiGeo_Discriminative_Geometry-Aware_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_DiGeo_Discriminative_Geometry-Aware_Learning_for_Generalized_Few-Shot_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_DiGeo_Discriminative_Geometry-Aware_Learning_for_Generalized_Few-Shot_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation | Bo Huang, Mingyang Chen, Yi Wang, Junda Lu, Minhao Cheng, Wei Wang | Distilled student models in teacher-student architectures are widely considered for computational-effective deployment in real-time applications and edge devices. However, there is a higher risk of student models to encounter adversarial attacks at the edge. Popular enhancing schemes such as adversarial training have limited performance on compressed networks. Thus, recent studies concern about adversarial distillation (AD) that aims to inherit not only prediction accuracy but also adversarial robustness of a robust teacher model under the paradigm of robust optimization. In the min-max framework of AD, existing AD methods generally use fixed supervision information from the teacher model to guide the inner optimization for knowledge distillation which often leads to an overcorrection towards model smoothness. In this paper, we propose an adaptive adversarial distillation (AdaAD) that involves the teacher model in the knowledge optimization process in a way interacting with the student model to adaptively search for the inner results. Comparing with state-of-the-art methods, the proposed AdaAD can significantly boost both the prediction accuracy and adversarial robustness of student models in most scenarios. In particular, the ResNet-18 model trained by AdaAD achieves top-rank performance (54.23% robust accuracy) on RobustBench under AutoAttack. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Boosting_Accuracy_and_Robustness_of_Student_Models_via_Adaptive_Adversarial_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Boosting_Accuracy_and_Robustness_of_Student_Models_via_Adaptive_Adversarial_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Boosting_Accuracy_and_Robustness_of_Student_Models_via_Adaptive_Adversarial_CVPR_2023_paper.html | CVPR 2023 | null |
METransformer: Radiology Report Generation by Transformer With Multiple Learnable Expert Tokens | Zhanyu Wang, Lingqiao Liu, Lei Wang, Luping Zhou | In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable "expert" tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their overlap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing "single-expert" models to further improve its performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_METransformer_Radiology_Report_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.html | CVPR 2023 | null |
PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing | Yichen Sheng, Jianming Zhang, Julien Philip, Yannick Hold-Geoffroy, Xin Sun, He Zhang, Lu Ling, Bedrich Benes | Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections. However, the lack of geometry limits the quality of the generated soft shadows and constrains reflections to pure specular ones. We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel height representation to 3D space. Using this mapping, PixHt-Lab reconstructs both the cutout and background geometry and renders realistic, diverse, lighting effects for image compositing. Given a surface with physically-based materials, we can render reflections with varying glossiness. To generate more realistic soft shadows, we further propose to use 3D-aware buffer channels to guide a neural renderer. Both quantitative and qualitative evaluations demonstrate that PixHt-Lab significantly improves soft shadow generation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sheng_PixHt-Lab_Pixel_Height_Based_Light_Effect_Generation_for_Image_Compositing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sheng_PixHt-Lab_Pixel_Height_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sheng_PixHt-Lab_Pixel_Height_Based_Light_Effect_Generation_for_Image_Compositing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sheng_PixHt-Lab_Pixel_Height_Based_Light_Effect_Generation_for_Image_Compositing_CVPR_2023_paper.html | CVPR 2023 | null |
A Soma Segmentation Benchmark in Full Adult Fly Brain | Xiaoyu Liu, Bo Hu, Mingxing Li, Wei Huang, Yueyi Zhang, Zhiwei Xiong | Neuron reconstruction in a full adult fly brain from high-resolution electron microscopy (EM) data is regarded as a cornerstone for neuroscientists to explore how neurons inspire intelligence. As the central part of neurons, somas in the full brain indicate the origin of neurogenesis and neural functions. However, due to the absence of EM datasets specifically annotated for somas, existing deep learning-based neuron reconstruction methods cannot directly provide accurate soma distribution and morphology. Moreover, full brain neuron reconstruction remains extremely time-consuming due to the unprecedentedly large size of EM data. In this paper, we develop an efficient soma reconstruction method for obtaining accurate soma distribution and morphology information in a full adult fly brain. To this end, we first make a high-resolution EM dataset with fine-grained 3D manual annotations on somas. Relying on this dataset, we propose an efficient, two-stage deep learning algorithm for predicting accurate locations and boundaries of 3D soma instances. Further, we deploy a parallelized, high-throughput data processing pipeline for executing the above algorithm on the full brain. Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective. We release our code and dataset at https://github.com/liuxy1103/EMADS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_A_Soma_Segmentation_Benchmark_in_Full_Adult_Fly_Brain_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_A_Soma_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_A_Soma_Segmentation_Benchmark_in_Full_Adult_Fly_Brain_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_A_Soma_Segmentation_Benchmark_in_Full_Adult_Fly_Brain_CVPR_2023_paper.html | CVPR 2023 | null |
RGB No More: Minimally-Decoded JPEG Vision Transformers | Jeongsoo Park, Justin Johnson | Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk; decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training Vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data. Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting. With these two improvements -- ViT and data augmentation -- we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_RGB_No_More_Minimally-Decoded_JPEG_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_RGB_No_More_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16421 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_RGB_No_More_Minimally-Decoded_JPEG_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_RGB_No_More_Minimally-Decoded_JPEG_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Revealing the Dark Secrets of Masked Image Modeling | Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, Yue Cao | Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations and the experiments, to uncover their key representational differences. From the visualizations, we find that MIM brings locality inductive bias to all layers of the trained models, but supervised models tend to focus locally at lower layers but more globally at higher layers. That may be the reason why MIM helps Vision Transformers that have a very large receptive field to optimize. Using MIM, the model can maintain a large diversity on attention heads in all layers. But for supervised models, the diversity on attention heads almost disappears from the last three layers and less diversity harms the fine-tuning performance. From the experiments, we find that MIM models can perform significantly better on geometric and motion tasks with weak semantics or fine-grained classification tasks, than their supervised counterparts. Without bells and whistles, a standard MIM pre-trained SwinV2-L could achieve state-of-the-art performance on pose estimation (78.9 AP on COCO test-dev and 78.0 AP on CrowdPose), depth estimation (0.287 RMSE on NYUv2 and 1.966 RMSE on KITTI), and video object tracking (70.7 SUC on LaSOT). For the semantic understanding datasets where the categories are sufficiently covered by the supervised pre-training, MIM models can still achieve highly competitive transfer performance. With a deeper understanding of MIM, we hope that our work can inspire new and solid research in this direction. Code will be available at https://github.com/zdaxie/MIM-DarkSecrets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Revealing_the_Dark_Secrets_of_Masked_Image_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Revealing_the_Dark_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.13543 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Revealing_the_Dark_Secrets_of_Masked_Image_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Revealing_the_Dark_Secrets_of_Masked_Image_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Fine-Grained Classification With Noisy Labels | Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Chenhui Guo, Yilong Yin | Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-grained datasets (LNL-FG), which is more practical and challenging as large inter-class ambiguities among fine-grained classes cause more noisy labels. We empirically show that existing methods that work well for LNL fail to achieve satisfying performance for LNL-FG, arising the practical need of effective solutions for LNL-FG. To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation. Specifically, we design a noise-tolerated supervised contrastive learning loss that incorporates a weight-aware mechanism for noisy label correction and selectively updating momentum queue lists. By this mechanism, we mitigate the effects of noisy anchors and avoid inserting noisy labels into the momentum-updated queue. Besides, to avoid manually-defined augmentation strategies in contrastive learning, we propose an efficient stochastic module that samples feature embeddings from a generated distribution, which can also enhance the representation ability of deep models. SNSCL is general and compatible with prevailing robust LNL strategies to improve their performance for LNL-FG. Extensive experiments demonstrate the effectiveness of SNSCL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Fine-Grained_Classification_With_Noisy_Labels_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Fine-Grained_Classification_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02404 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Fine-Grained_Classification_With_Noisy_Labels_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Fine-Grained_Classification_With_Noisy_Labels_CVPR_2023_paper.html | CVPR 2023 | null |
CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation Loss | Nurbek Tastan, Karthik Nandakumar | Large volumes of data required to train accurate deep neural networks (DNNs) are seldom available with any single entity. Often, privacy concerns and stringent data regulations prevent entities from sharing data with each other or with a third-party learning service provider. While cross-silo federated learning (FL) allows collaborative learning of large DNNs without sharing the data itself, most existing cross-silo FL algorithms have an unacceptable utility-privacy trade-off. In this work, we propose a framework called Confidential and Private Decentralized (CaPriDe) learning, which optimally leverages the power of fully homomorphic encryption (FHE) to enable collaborative learning without compromising on the confidentiality and privacy of data. In CaPriDe learning, participating entities release their private data in an encrypted form allowing other participants to perform inference in the encrypted domain. The crux of CaPriDe learning is mutual knowledge distillation between multiple local models through a novel distillation loss, which is an approximation of the Kullback-Leibler (KL) divergence between the local predictions and encrypted inferences of other participants on the same data that can be computed in the encrypted domain. Extensive experiments on three datasets show that CaPriDe learning can improve the accuracy of local models without any central coordination, provide strong guarantees of data confidentiality and privacy, and has the ability to handle statistical heterogeneity. Constraints on the model architecture (arising from the need to be FHE-friendly), limited scalability, and computational complexity of encrypted domain inference are the main limitations of the proposed approach. The code can be found at https://github.com/tnurbek/capride-learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tastan_CaPriDe_Learning_Confidential_and_Private_Decentralized_Learning_Based_on_Encryption-Friendly_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tastan_CaPriDe_Learning_Confidential_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tastan_CaPriDe_Learning_Confidential_and_Private_Decentralized_Learning_Based_on_Encryption-Friendly_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tastan_CaPriDe_Learning_Confidential_and_Private_Decentralized_Learning_Based_on_Encryption-Friendly_CVPR_2023_paper.html | CVPR 2023 | null |
Hybrid Active Learning via Deep Clustering for Video Action Detection | Aayush J. Rana, Yogesh S. Rawat | In this work, we focus on reducing the annotation cost for video action detection which requires costly frame-wise dense annotations. We study a novel hybrid active learning (AL) strategy which performs efficient labeling using both intra-sample and inter-sample selection. The intra-sample selection leads to labeling of fewer frames in a video as opposed to inter-sample selection which operates at video level. This hybrid strategy reduces the annotation cost from two different aspects leading to significant labeling cost reduction. The proposed approach utilize Clustering-Aware Uncertainty Scoring (CLAUS), a novel label acquisition strategy which relies on both informativeness and diversity for sample selection. We also propose a novel Spatio-Temporal Weighted (STeW) loss formulation, which helps in model training under limited annotations. The proposed approach is evaluated on UCF-101-24 and J-HMDB-21 datasets demonstrating its effectiveness in significantly reducing the annotation cost where it consistently outperforms other baselines. Project details available at https://sites.google.com/view/activesparselabeling/home | https://openaccess.thecvf.com/content/CVPR2023/papers/Rana_Hybrid_Active_Learning_via_Deep_Clustering_for_Video_Action_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rana_Hybrid_Active_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rana_Hybrid_Active_Learning_via_Deep_Clustering_for_Video_Action_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rana_Hybrid_Active_Learning_via_Deep_Clustering_for_Video_Action_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Fine-Grained Image-Text Matching by Cross-Modal Hard Aligning Network | Zhengxin Pan, Fangyu Wu, Bailing Zhang | Current state-of-the-art image-text matching methods implicitly align the visual-semantic fragments, like regions in images and words in sentences, and adopt cross-attention mechanism to discover fine-grained cross-modal semantic correspondence. However, the cross-attention mechanism may bring redundant or irrelevant region-word alignments, degenerating retrieval accuracy and limiting efficiency. Although many researchers have made progress in mining meaningful alignments and thus improving accuracy, the problem of poor efficiency remains unresolved. In this work, we propose to learn fine-grained image-text matching from the perspective of information coding. Specifically, we suggest a coding framework to explain the fragments aligning process, which provides a novel view to reexamine the cross-attention mechanism and analyze the problem of redundant alignments. Based on this framework, a Cross-modal Hard Aligning Network (CHAN) is designed, which comprehensively exploits the most relevant region-word pairs and eliminates all other alignments. Extensive experiments conducted on two public datasets, MS-COCO and Flickr30K, verify that the relevance of the most associated word-region pairs is discriminative enough as an indicator of the image-text similarity, with superior accuracy and efficiency over the state-of-the-art approaches on the bidirectional image and text retrieval tasks. Our code will be available at https://github.com/ppanzx/CHAN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Fine-Grained_Image-Text_Matching_by_Cross-Modal_Hard_Aligning_Network_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Fine-Grained_Image-Text_Matching_by_Cross-Modal_Hard_Aligning_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Fine-Grained_Image-Text_Matching_by_Cross-Modal_Hard_Aligning_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers | Cong Wei, Brendan Duke, Ruowei Jiang, Parham Aarabi, Graham W. Taylor, Florian Shkurti | Vision Transformers (ViT) have shown competitive advantages in terms of performance compared to convolutional neural networks (CNNs), though they often come with high computational costs. To this end, previous methods explore different attention patterns by limiting a fixed number of spatially nearby tokens to accelerate the ViT's multi-head self-attention (MHSA) operations. However, such structured attention patterns limit the token-to-token connections to their spatial relevance, which disregards learned semantic connections from a full attention mask. In this work, we propose an approach to learn instance-dependent attention patterns, by devising a lightweight connectivity predictor module that estimates the connectivity score of each pair of tokens. Intuitively, two tokens have high connectivity scores if the features are considered relevant either spatially or semantically. As each token only attends to a small number of other tokens, the binarized connectivity masks are often very sparse by nature and therefore provide the opportunity to reduce network FLOPs via sparse computations. Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto frontier between FLOPs and top-1 accuracy on ImageNet compared to token sparsity. Our method reduces 48% 69% FLOPs of MHSA while the accuracy drop is within 0.4%. We also show that combining attention and token sparsity reduces ViT FLOPs by over 60%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Sparsifiner_Learning_Sparse_Instance-Dependent_Attention_for_Efficient_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Sparsifiner_Learning_Sparse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13755 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Sparsifiner_Learning_Sparse_Instance-Dependent_Attention_for_Efficient_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Sparsifiner_Learning_Sparse_Instance-Dependent_Attention_for_Efficient_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Structured Sparsity Learning for Efficient Video Super-Resolution | Bin Xia, Jingwen He, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Luc Van Gool | The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, e.g., smartphones and drones. Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively. The code is available at https://github.com/Zj-BinXia/SSL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xia_Structured_Sparsity_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.07687 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling | Daizong Ding, Erling Jiang, Yuanmin Huang, Mi Zhang, Wenxuan Li, Min Yang | Recently, deep neural networks have shown great success on 3D point cloud classification tasks, which simultaneously raises the concern of adversarial attacks that cause severe damage to real-world applications. Moreover, defending against adversarial examples in point cloud data is extremely difficult due to the emergence of various attack strategies. In this work, with the insight of the fact that the adversarial examples in this task still preserve the same semantic and structural information as the original input, we design a novel defense framework for improving the robustness of existing classification models, which consists of two main modules: the attention-based pooling and the dynamic contrastive learning. In addition, we also develop an algorithm to theoretically certify the robustness of the proposed framework. Extensive empirical results on two datasets and three classification models show the robustness of our approach against various attacks, e.g., the averaged attack success rate of PointNet decreases from 70.2% to 2.7% on the ModelNet40 dataset under 9 common attacks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_CAP_Robust_Point_Cloud_Classification_via_Semantic_and_Structural_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_CAP_Robust_Point_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_CAP_Robust_Point_Cloud_Classification_via_Semantic_and_Structural_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_CAP_Robust_Point_Cloud_Classification_via_Semantic_and_Structural_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
"Seeing" Electric Network Frequency From Events | Lexuan Xu, Guang Hua, Haijian Zhang, Lei Yu, Ning Qiao | Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, motion, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we first formulate and validate the physical mechanism for the ENF captured in events, and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. Furthermore, we build an Event-Video ENF Dataset (EV-ENFD) that records both events and videos in diverse scenes. Extensive experiments on EV-ENFD demonstrate that our proposed E-ENF method can extract more accurate ENF traces, outperforming the conventional V-ENF by a large margin, especially in challenging environments with object motions and extreme lighting conditions. The code and dataset are available at https://github.com/xlx-creater/E-ENF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Seeing_Electric_Network_Frequency_From_Events_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.02597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Seeing_Electric_Network_Frequency_From_Events_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Seeing_Electric_Network_Frequency_From_Events_CVPR_2023_paper.html | CVPR 2023 | null |
MMVC: Learned Multi-Mode Video Compression With Block-Based Prediction Mode Selection and Density-Adaptive Entropy Coding | Bowen Liu, Yu Chen, Rakesh Chowdary Machineni, Shiyu Liu, Hun-Seok Kim | Learning-based video compression has been extensively studied over the past years, but it still has limitations in adapting to various motion patterns and entropy models. In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns. Proposed multi-modes include ConvLSTM-based feature domain prediction, optical flow conditioned feature domain prediction, and feature propagation to address a wide range of cases from static scenes without apparent motions to dynamic scenes with a moving camera. We partition the feature space into blocks for temporal prediction in spatial block-based representations. For entropy coding, we consider both dense and sparse post-quantization residual blocks, and apply optional run-length coding to sparse residuals to improve the compression rate. In this sense, our method uses a dual-mode entropy coding scheme guided by a binary density map, which offers significant rate reduction surpassing the extra cost of transmitting the binary selection map. We validate our scheme with some of the most popular benchmarking datasets. Compared with state-of-the-art video compression schemes and standard codecs, our method yields better or competitive results measured with PSNR and MS-SSIM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_MMVC_Learned_Multi-Mode_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02273 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.html | CVPR 2023 | null |
Visual-Tactile Sensing for In-Hand Object Reconstruction | Wenqiang Xu, Zhenjun Yu, Han Xue, Ruolin Ye, Siqiong Yao, Cewu Lu | Tactile sensing is one of the modalities human rely on heavily to perceive the world. Working with vision, this modality refines local geometry structure, measures deformation at contact area, and indicates hand-object contact state. With the availability of open-source tactile sensors such as DIGIT, research on visual-tactile learning is becoming more accessible and reproducible. Leveraging this tactile sensor, we propose a novel visual-tactile in-hand object reconstruction framework VTacO, and extend it to VTacOH for hand-object reconstruction. Since our method can support both rigid and deformable object reconstruction, and no existing benchmark are proper for the goal. We propose a simulation environment, VT-Sim, which supports to generate hand-object interaction for both rigid and deformable objects. With VT-Sim, we generate a large-scale training dataset, and evaluate our method on it. Extensive experiments demonstrate that our proposed method can outperform the previous baseline methods qualitatively and quantitatively. Finally, we directly apply our model trained in simulation to various real-world test cases, which display qualitative results. Codes, models, simulation environment, datasets will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Visual-Tactile_Sensing_for_In-Hand_Object_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Visual-Tactile_Sensing_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14498 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Visual-Tactile_Sensing_for_In-Hand_Object_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Visual-Tactile_Sensing_for_In-Hand_Object_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
vMAP: Vectorised Object Mapping for Neural Field SLAM | Xin Kong, Shikun Liu, Marwan Taher, Andrew J. Davison | We present vMAP, an object-level dense SLAM system using neural field representations. Each object is represented by a small MLP, enabling efficient, watertight object modelling without the need for 3D priors. As an RGB-D camera browses a scene with no prior information, vMAP detects object instances on-the-fly, and dynamically adds them to its map. Specifically, thanks to the power of vectorised training, vMAP can optimise as many as 50 individual objects in a single scene, with an extremely efficient training speed of 5Hz map update. We experimentally demonstrate significantly improved scene-level and object-level reconstruction quality compared to prior neural field SLAM systems. Project page: https://kxhit.github.io/vMAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_vMAP_Vectorised_Object_Mapping_for_Neural_Field_SLAM_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_vMAP_Vectorised_Object_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.01838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_vMAP_Vectorised_Object_Mapping_for_Neural_Field_SLAM_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_vMAP_Vectorised_Object_Mapping_for_Neural_Field_SLAM_CVPR_2023_paper.html | CVPR 2023 | null |
Images Speak in Images: A Generalist Painter for In-Context Visual Learning | Xinlong Wang, Wen Wang, Yue Cao, Chunhua Shen, Tiejun Huang | In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. But in computer vision, the difficulties for in-context learning lie in that tasks vary significantly in the output representations, thus it is unclear how to define the general-purpose task prompts that the vision model can understand and transfer to out-of-domain tasks. In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images. With this idea, our training process is extremely simple, which performs standard masked image modeling on the stitch of input and output image pairs. This makes the model capable of performing tasks conditioned on visible image patches. Thus, during inference, we can adopt a pair of input and output images from the same task as the input condition, to indicate which task to perform. Without bells and whistles, our generalist Painter can achieve competitive performance compared to well-established task-specific models, on seven representative vision tasks ranging from high-level visual understanding to low-level image processing. In addition, Painter significantly outperforms recent generalist models on several challenging tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Images_Speak_in_Images_A_Generalist_Painter_for_In-Context_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Images_Speak_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02499 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Images_Speak_in_Images_A_Generalist_Painter_for_In-Context_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Images_Speak_in_Images_A_Generalist_Painter_for_In-Context_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
Omni Aggregation Networks for Lightweight Image Super-Resolution | Hang Wang, Xuanhong Chen, Bingbing Ni, Yutian Liu, Jinfan Liu | While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions. To tackle these drawbacks, this work proposes two enhanced components under a new Omni-SR architecture. First, an Omni Self-Attention (OSA) paradigm is proposed based on dense interaction principle, which can simultaneously model pixel-interaction from both spatial and channel dimensions, mining the potential correlations across omni-axis (i.e., spatial and channel). Coupling with mainstream window partitioning strategies, OSA can achieve superior performance with compelling computational budgets. Second, a multi-scale interaction scheme is proposed to mitigate sub-optimal ERF (i.e., premature saturation) in shallow models, which facilitates local propagation and meso-/global-scale interactions, rendering a omni-scale aggregation building block. Extensive experiments demonstrate that Omni-SR achieves record-high performance on lightweight super-resolution benchmarks (e.g., 26.95dB@Urban100 x4 with only 792K parameters). Our code is available at https://github.com/Francis0625/Omni-SR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Omni_Aggregation_Networks_for_Lightweight_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Omni_Aggregation_Networks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Omni_Aggregation_Networks_for_Lightweight_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Omni_Aggregation_Networks_for_Lightweight_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
StyLess: Boosting the Transferability of Adversarial Examples | Kaisheng Liang, Bin Xiao | Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible perturbations to benign examples. The attack transferability enables adversarial examples to attack black-box DNNs with unknown architectures or parameters, which poses threats to many real-world applications. We find that existing transferable attacks do not distinguish between style and content features during optimization, limiting their attack transferability. To improve attack transferability, we propose a novel attack method called style-less perturbation (StyLess). Specifically, instead of using a vanilla network as the surrogate model, we advocate using stylized networks, which encode different style features by perturbing an adaptive instance normalization. Our method can prevent adversarial examples from using non-robust style features and help generate transferable perturbations. Comprehensive experiments show that our method can significantly improve the transferability of adversarial examples. Furthermore, our approach is generic and can outperform state-of-the-art transferable attacks when combined with other attack techniques. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_StyLess_Boosting_the_Transferability_of_Adversarial_Examples_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.11579 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_StyLess_Boosting_the_Transferability_of_Adversarial_Examples_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_StyLess_Boosting_the_Transferability_of_Adversarial_Examples_CVPR_2023_paper.html | CVPR 2023 | null |
Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields | Yue Chen, Xingyu Chen, Xuan Wang, Qi Zhang, Yu Guo, Ying Shan, Fei Wang | Neural Radiance Fields (NeRF) have achieved photorealistic novel views synthesis; however, the requirement of accurate camera poses limits its application. Despite analysis-by-synthesis extensions for jointly learning neural 3D representations and registering camera frames exist, they are susceptible to suboptimal solutions if poorly initialized. We propose L2G-NeRF, a Local-to-Global registration method for bundle-adjusting Neural Radiance Fields: first, a pixel-wise flexible alignment, followed by a frame-wise constrained parametric alignment. Pixel-wise local alignment is learned in an unsupervised way via a deep network which optimizes photometric reconstruction errors. Frame-wise global alignment is performed using differentiable parameter estimation solvers on the pixel-wise correspondences to find a global transformation. Experiments on synthetic and real-world data show that our method outperforms the current state-of-the-art in terms of high-fidelity reconstruction and resolving large camera pose misalignment. Our module is an easy-to-use plugin that can be applied to NeRF variants and other neural field applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Local-to-Global_Registration_for_Bundle-Adjusting_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Local-to-Global_Registration_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11505 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Local-to-Global_Registration_for_Bundle-Adjusting_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Local-to-Global_Registration_for_Bundle-Adjusting_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection | Fan Lu, Kai Zhu, Wei Zhai, Kecheng Zheng, Yang Cao | Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set. The coexistence of in-distribution and out-of-distribution samples will exacerbate the model overfitting when no distinction is made. To address this problem, we propose a novel uncertainty-aware optimal transport scheme. Our scheme consists of an energy-based transport (ET) mechanism that estimates the fluctuating cost of uncertainty to promote the assignment of semantic-agnostic representation, and an inter-cluster extension strategy that enhances the discrimination of semantic property among different clusters by widening the corresponding margin distance. Furthermore, a T-energy score is presented to mitigate the magnitude gap between the parallel transport and classifier branches. Extensive experiments on two standard SCOOD benchmarks demonstrate the above-par OOD detection performance, outperforming the state-of-the-art methods by a margin of 27.69% and 34.4% on FPR@95, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Uncertainty-Aware_Optimal_Transport_for_Semantically_Coherent_Out-of-Distribution_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Uncertainty-Aware_Optimal_Transport_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10449 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Uncertainty-Aware_Optimal_Transport_for_Semantically_Coherent_Out-of-Distribution_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Uncertainty-Aware_Optimal_Transport_for_Semantically_Coherent_Out-of-Distribution_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
FJMP: Factorized Joint Multi-Agent Motion Prediction Over Learned Directed Acyclic Interaction Graphs | Luke Rowe, Martin Ethier, Eli-Henry Dykhne, Krzysztof Czarnecki | Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving scenarios. FJMP models the future scene interaction dynamics as a sparse directed interaction graph, where edges denote explicit interactions between agents. We then prune the graph into a directed acyclic graph (DAG) and decompose the joint prediction task into a sequence of marginal and conditional predictions according to the partial ordering of the DAG, where joint future trajectories are decoded using a directed acyclic graph neural network (DAGNN). We conduct experiments on the INTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches, especially on the most interactive and kinematically interesting agents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rowe_FJMP_Factorized_Joint_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16197 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-Language | Chuanhao Li, Zhen Li, Chenchen Jing, Yunde Jia, Yuwei Wu | Compositionality is one of the fundamental properties of human cognition (Fodor & Pylyshyn, 1988). Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community. It is essential to understand the effect of the primitives, including words, image regions, and video frames, to improve the compositional generalization capability. In this paper, we explore the effect of primitives for compositional generalization in V&L. Specifically, we present a self-supervised learning based framework that equips V&L methods with two characteristics: semantic equivariance and semantic invariance. With the two characteristics, the methods understand primitives by perceiving the effect of primitive changes on sample semantics and ground-truth. Experimental results on two tasks: temporal video grounding and visual question answering, demonstrate the effectiveness of our framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Exploring_the_Effect_of_Primitives_for_Compositional_Generalization_in_Vision-and-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Exploring_the_Effect_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Exploring_the_Effect_of_Primitives_for_Compositional_Generalization_in_Vision-and-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Exploring_the_Effect_of_Primitives_for_Compositional_Generalization_in_Vision-and-Language_CVPR_2023_paper.html | CVPR 2023 | null |
Correlational Image Modeling for Self-Supervised Visual Pre-Training | Wei Li, Jiahao Xie, Chen Change Loy | We introduce Correlational Image Modeling (CIM), a novel but surprisingly effective approach to self-supervised visual pre-training. Our CIM performs a simple pretext task: we randomly crop image regions (exemplar) from an input image (context) and predict correlation maps between the exemplars and the context. Three key designs enable correlational image modeling as a nontrivial and meaningful self-supervisory task. First, to generate useful exemplar-context pairs, we consider cropping image regions with various scales, shapes, rotations, and transformations. Second, we employ a bootstrap learning framework that involves online and target networks. During pre-training, the former takes exemplars as inputs while the latter converts the context. Third, we model the output correlation maps via a simple cross-attention block, within which the context serves as queries and the exemplars offer values and keys. We show that CIM performs on par or better than the current state of the art on self-supervised and transfer benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Correlational_Image_Modeling_for_Self-Supervised_Visual_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Correlational_Image_Modeling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Correlational_Image_Modeling_for_Self-Supervised_Visual_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Correlational_Image_Modeling_for_Self-Supervised_Visual_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
DC2: Dual-Camera Defocus Control by Learning To Refocus | Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang, Ying Chen Lou, Jia-Bin Huang, Abhishek Kar | Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements. However, fixed aperture remains a key limitation, preventing users from controlling the depth of field (DoF) of captured images. At the same time, many smartphones now have multiple cameras with different fixed apertures - specifically, an ultra-wide camera with wider field of view and deeper DoF and a higher resolution primary camera with shallower DoF. In this work, we propose DC^2, a system for defocus control for synthetically varying camera aperture, focus distance and arbitrary defocus effects by fusing information from such a dual-camera system. Our key insight is to leverage real-world smartphone camera dataset by using image refocus as a proxy task for learning to control defocus. Quantitative and qualitative evaluations on real-world data demonstrate our system's efficacy where we outperform state-of-the-art on defocus deblurring, bokeh rendering, and image refocus. Finally, we demonstrate creative post-capture defocus control enabled by our method, including tilt-shift and content-based defocus effects. | https://openaccess.thecvf.com/content/CVPR2023/papers/Alzayer_DC2_Dual-Camera_Defocus_Control_by_Learning_To_Refocus_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Alzayer_DC2_Dual-Camera_Defocus_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Alzayer_DC2_Dual-Camera_Defocus_Control_by_Learning_To_Refocus_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Alzayer_DC2_Dual-Camera_Defocus_Control_by_Learning_To_Refocus_CVPR_2023_paper.html | CVPR 2023 | null |
MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence | Yixuan Sun, Yiwen Huang, Haijing Guo, Yuzhou Zhao, Runmin Wu, Yizhou Yu, Weifeng Ge, Wenqiang Zhang | Semantic correspondence have built up a new way for object recognition. However current single-object matching schema can be hard for discovering commonalities for a category and far from the real-world recognition tasks. To fill this gap, we design the multi-instance semantic correspondence task which aims at constructing the correspondence between multiple objects in an image pair. To support this task, we build a multi-instance semantic correspondence (MISC) dataset from COCO Detection 2017 task called MISC210K. We construct our dataset as three steps: (1) category selection and data cleaning; (2) keypoint design based on 3D models and object description rules; (3) human-machine collaborative annotation. Following these steps, we select 34 classes of objects with 4,812 challenging images annotated via a well designed semi-automatic workflow, and finally acquire 218,179 image pairs with instance masks and instance-level keypoint pairs annotated. We design a dual-path collaborative learning pipeline to train instance-level co-segmentation task and fine-grained level correspondence task together. Benchmark evaluation and further ablation results with detailed analysis are provided with three future directions proposed. Our project is available on https://github.com/YXSUNMADMAX/MISC210K. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_MISC210K_A_Large-Scale_Dataset_for_Multi-Instance_Semantic_Correspondence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_MISC210K_A_Large-Scale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_MISC210K_A_Large-Scale_Dataset_for_Multi-Instance_Semantic_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_MISC210K_A_Large-Scale_Dataset_for_Multi-Instance_Semantic_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Implicit Glyph Attention for Text Recognition | Tongkun Guan, Chaochen Gu, Jingzheng Tu, Xue Yang, Qi Feng, Yudi Zhao, Wei Shen | The attention mechanism has become the de facto module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These methods can be summarized into implicit attention based and supervised attention based, depended on how the attention is computed, i.e., implicit attention and supervised attention are learned from sequence-level text annotations and character-level bounding box annotations, respectively. Implicit attention, as it may extract coarse or even incorrect spatial regions as character attention, is prone to suffering from an alignment-drifted issue. Supervised attention can alleviate the above issue, but it is category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when the number of character categories is large. To address the aforementioned issues, we propose a novel attention mechanism for STR, self-supervised implicit glyph attention (SIGA). SIGA delineates the glyph structures of text images by jointly self-supervised text segmentation and implicit attention alignment, which serve as the supervision to improve attention correctness without extra character-level annotations. Experimental results demonstrate that SIGA performs consistently and significantly better than previous attention-based STR methods, in terms of both attention correctness and final recognition performance on publicly available context benchmarks and our contributed contextless benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guan_Self-Supervised_Implicit_Glyph_Attention_for_Text_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guan_Self-Supervised_Implicit_Glyph_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2203.03382 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guan_Self-Supervised_Implicit_Glyph_Attention_for_Text_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guan_Self-Supervised_Implicit_Glyph_Attention_for_Text_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
ACL-SPC: Adaptive Closed-Loop System for Self-Supervised Point Cloud Completion | Sangmin Hong, Mohsen Yavartanoo, Reyhaneh Neshatavar, Kyoung Mu Lee | Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been steep progress in the supervised methods on the synthetic point cloud completion task, it is hardly applicable in real-world scenarios due to the domain gap between the synthetic and real-world datasets or the requirement of prior information. To overcome these limitations, we propose a novel self-supervised framework ACL-SPC for point cloud completion to train and test on the same data. ACL-SPC takes a single partial input and attempts to output the complete point cloud using an adaptive closed-loop (ACL) system that enforces the output same for the variation of an input. We evaluate our ACL-SPC on various datasets to prove that it can successfully learn to complete a partial point cloud as the first self-supervised scheme. Results show that our method is comparable with unsupervised methods and achieves superior performance on the real-world dataset compared to the supervised methods trained on the synthetic dataset. Extensive experiments justify the necessity of self-supervised learning and the effectiveness of our proposed method for the real-world point cloud completion task. The code is publicly available from this link. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hong_ACL-SPC_Adaptive_Closed-Loop_System_for_Self-Supervised_Point_Cloud_Completion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hong_ACL-SPC_Adaptive_Closed-Loop_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hong_ACL-SPC_Adaptive_Closed-Loop_System_for_Self-Supervised_Point_Cloud_Completion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hong_ACL-SPC_Adaptive_Closed-Loop_System_for_Self-Supervised_Point_Cloud_Completion_CVPR_2023_paper.html | CVPR 2023 | null |
MAGE: MAsked Generative Encoder To Unify Representation Learning and Image Synthesis | Tianhong Li, Huiwen Chang, Shlok Mishra, Han Zhang, Dina Katabi, Dilip Krishnan | Generative modeling and representation learning are two key tasks in computer vision. However, these models are typically trained independently, which ignores the potential for each task to help the other, and leads to training and model maintenance overheads. In this work, we propose MAsked Generative Encoder (MAGE), the first framework to unify SOTA image generation and self-supervised representation learning. Our key insight is that using variable masking ratios in masked image modeling pre-training can allow generative training (very high masking ratio) and representation learning (lower masking ratio) under the same training framework. Inspired by previous generative models, MAGE uses semantic tokens learned by a vector-quantized GAN at inputs and outputs, combining this with masking. We can further improve the representation by adding a contrastive loss to the encoder output. We extensively evaluate the generation and representation learning capabilities of MAGE. On ImageNet-1K, a single MAGE ViT-L model obtains 9.10 FID in the task of class-unconditional image generation and 78.9% top-1 accuracy for linear probing, achieving state-of-the-art performance in both image generation and representation learning. Code is available at https://github.com/LTH14/mage. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_MAGE_MAsked_Generative_Encoder_To_Unify_Representation_Learning_and_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_MAGE_MAsked_Generative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09117 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MAGE_MAsked_Generative_Encoder_To_Unify_Representation_Learning_and_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MAGE_MAsked_Generative_Encoder_To_Unify_Representation_Learning_and_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Focus on Details: Online Multi-Object Tracking With Diverse Fine-Grained Representation | Hao Ren, Shoudong Han, Huilin Ding, Ziwen Zhang, Hongwei Wang, Faquan Wang | Discriminative representation is essential to keep a unique identifier for each target in Multiple object tracking (MOT). Some recent MOT methods extract features of the bounding box region or the center point as identity embeddings. However, when targets are occluded, these coarse-grained global representations become unreliable. To this end, we propose exploring diverse fine-grained representation, which describes appearance comprehensively from global and local perspectives. This fine-grained representation requires high feature resolution and precise semantic information. To effectively alleviate the semantic misalignment caused by indiscriminate contextual information aggregation, Flow Alignment FPN (FAFPN) is proposed for multi-scale feature alignment aggregation. It generates semantic flow among feature maps from different resolutions to transform their pixel positions. Furthermore, we present a Multi-head Part Mask Generator (MPMG) to extract fine-grained representation based on the aligned feature maps. Multiple parallel branches of MPMG allow it to focus on different parts of targets to generate local masks without label supervision. The diverse details in target masks facilitate fine-grained representation. Eventually, benefiting from a Shuffle-Group Sampling (SGS) training strategy with positive and negative samples balanced, we achieve state-of-the-art performance on MOT17 and MOT20 test sets. Even on DanceTrack, where the appearance of targets is extremely similar, our method significantly outperforms ByteTrack by 5.0% on HOTA and 5.6% on IDF1. Extensive experiments have proved that diverse fine-grained representation makes Re-ID great again in MOT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Focus_on_Details_Online_Multi-Object_Tracking_With_Diverse_Fine-Grained_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_Focus_on_Details_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14589 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Focus_on_Details_Online_Multi-Object_Tracking_With_Diverse_Fine-Grained_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Focus_on_Details_Online_Multi-Object_Tracking_With_Diverse_Fine-Grained_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
DiffPose: Toward More Reliable 3D Pose Estimation | Jia Gong, Lin Geng Foo, Zhipeng Fan, Qiuhong Ke, Hossein Rahmani, Jun Liu | Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diffusion models have recently emerged as an effective tool for generating high-quality images from noise. Inspired by their capability, we explore a novel pose estimation framework (DiffPose) that formulates 3D pose estimation as a reverse diffusion process. We incorporate novel designs into our DiffPose to facilitate the diffusion process for 3D pose estimation: a pose-specific initialization of pose uncertainty distributions, a Gaussian Mixture Model-based forward diffusion process, and a context-conditioned reverse diffusion process. Our proposed DiffPose significantly outperforms existing methods on the widely used pose estimation benchmarks Human3.6M and MPI-INF-3DHP. Project page: https://gongjia0208.github.io/Diffpose/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gong_DiffPose_Toward_More_Reliable_3D_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gong_DiffPose_Toward_More_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16940 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gong_DiffPose_Toward_More_Reliable_3D_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gong_DiffPose_Toward_More_Reliable_3D_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field | Leheng Li, Qing Lian, Luozhou Wang, Ningning Ma, Ying-Cong Chen | This work explores the use of 3D generative models to synthesize training data for 3D vision tasks. The key requirements of the generative models are that the generated data should be photorealistic to match the real-world scenarios, and the corresponding 3D attributes should be aligned with given sampling labels. However, we find that the recent NeRF-based 3D GANs hardly meet the above requirements due to their designed generation pipeline and the lack of explicit 3D supervision. In this work, we propose Lift3D, an inverted 2D-to-3D generation framework to achieve the data generation objectives. Lift3D has several merits compared to prior methods: (1) Unlike previous 3D GANs that the output resolution is fixed after training, Lift3D can generalize to any camera intrinsic with higher resolution and photorealistic output. (2) By lifting well-disentangled 2D GAN to 3D object NeRF, Lift3D provides explicit 3D information of generated objects, thus offering accurate 3D annotations for downstream tasks. We evaluate the effectiveness of our framework by augmenting autonomous driving datasets. Experimental results demonstrate that our data generation framework can effectively improve the performance of 3D object detectors. Code: len-li.github.io/lift3d-web | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Lift3D_Synthesize_3D_Training_Data_by_Lifting_2D_GAN_to_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Lift3D_Synthesize_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03526 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Lift3D_Synthesize_3D_Training_Data_by_Lifting_2D_GAN_to_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Lift3D_Synthesize_3D_Training_Data_by_Lifting_2D_GAN_to_CVPR_2023_paper.html | CVPR 2023 | null |
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation | Xiaoyang Wang, Bingfeng Zhang, Limin Yu, Jimin Xiao | Recent semi-supervised semantic segmentation methods combine pseudo labeling and consistency regularization to enhance model generalization from perturbation-invariant training. In this work, we argue that adequate supervision can be extracted directly from the geometry of feature space. Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions within feature clusters defined by label and pseudo labels. The hypothesis is that lower-density features tend to be under-trained compared with those densely gathered. Therefore, we propose to apply regularization on the structure of the cluster by tackling the sparsity to increase intra-class compactness in feature space. With this goal, we present a Density-Guided Contrastive Learning (DGCL) strategy to push anchor features in sparse regions toward cluster centers approximated by high-density positive keys. The heart of our method is to estimate feature density which is defined as neighbor compactness. We design a multi-scale density estimation module to obtain the density from multiple nearest-neighbor graphs for robust density modeling. Moreover, a unified training framework is proposed to combine label-guided self-training and density-guided geometry regularization to form complementary supervision on unlabeled data. Experimental results on PASCAL VOC and Cityscapes under various semi-supervised settings demonstrate that our proposed method achieves state-of-the-art performances. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Hunting_Sparsity_Density-Guided_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Analytical Posterior Probability for Human Mesh Recovery | Qi Fang, Kang Chen, Yinghui Fan, Qing Shuai, Jiefeng Li, Weidong Zhang | Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks. To address such an issue, we derive a novel analytical formulation for learning posterior probability distributions of human joint rotations conditioned on bone directions in a Bayesian manner, and based on this, we propose a new posterior-guided framework for human mesh recovery. We demonstrate that our framework is not only superior to existing SOTA baselines on multiple benchmarks but also flexible enough to seamlessly incorporate with additional sensors due to its Bayesian nature. The code is available at https://github.com/NetEase-GameAI/ProPose. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_Learning_Analytical_Posterior_Probability_for_Human_Mesh_Recovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fang_Learning_Analytical_Posterior_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Learning_Analytical_Posterior_Probability_for_Human_Mesh_Recovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Learning_Analytical_Posterior_Probability_for_Human_Mesh_Recovery_CVPR_2023_paper.html | CVPR 2023 | null |
Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections | Jiaxiong Qiu, Peng-Tao Jiang, Yifan Zhu, Ze-Xin Yin, Ming-Ming Cheng, Bo Ren | Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in these scenes violates the multi-view consistency, then makes it challenging for recent methods to reconstruct target objects correctly. To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering. In NeuS-HSR, the object surface is parameterized as an implicit signed distance function (SDF). To reduce the interference of HSR, we propose decomposing the rendered image into two appearances: the target object and the auxiliary plane. We design a novel auxiliary plane module by combining physical assumptions and neural networks to generate the auxiliary plane appearance. Extensive experiments on synthetic and real-world datasets demonstrate that NeuS-HSR outperforms state-of-the-art approaches for accurate and robust target surface reconstruction against HSR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiu_Looking_Through_the_Glass_Neural_Surface_Reconstruction_Against_High_Specular_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiu_Looking_Through_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08706 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_Looking_Through_the_Glass_Neural_Surface_Reconstruction_Against_High_Specular_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_Looking_Through_the_Glass_Neural_Surface_Reconstruction_Against_High_Specular_CVPR_2023_paper.html | CVPR 2023 | null |
Non-Contrastive Unsupervised Learning of Physiological Signals From Video | Jeremy Speth, Nathan Vance, Patrick Flynn, Adam Czajka | Subtle periodic signals such as blood volume pulse and respiration can be extracted from RGB video, enabling noncontact health monitoring at low cost. Advancements in remote pulse estimation -- or remote photoplethysmography (rPPG) -- are currently driven by deep learning solutions. However, modern approaches are trained and evaluated on benchmark datasets with ground truth from contact-PPG sensors. We present the first non-contrastive unsupervised learning framework for signal regression to mitigate the need for labelled video data. With minimal assumptions of periodicity and finite bandwidth, our approach discovers the blood volume pulse directly from unlabelled videos. We find that encouraging sparse power spectra within normal physiological bandlimits and variance over batches of power spectra is sufficient for learning visual features of periodic signals. We perform the first experiments utilizing unlabelled video data not specifically created for rPPG to train robust pulse rate estimators. Given the limited inductive biases and impressive empirical results, the approach is theoretically capable of discovering other periodic signals from video, enabling multiple physiological measurements without the need for ground truth signals. | https://openaccess.thecvf.com/content/CVPR2023/papers/Speth_Non-Contrastive_Unsupervised_Learning_of_Physiological_Signals_From_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Speth_Non-Contrastive_Unsupervised_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Speth_Non-Contrastive_Unsupervised_Learning_of_Physiological_Signals_From_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Speth_Non-Contrastive_Unsupervised_Learning_of_Physiological_Signals_From_Video_CVPR_2023_paper.html | CVPR 2023 | null |
FashionSAP: Symbols and Attributes Prompt for Fine-Grained Fashion Vision-Language Pre-Training | Yunpeng Han, Lisai Zhang, Qingcai Chen, Zhijian Chen, Zhonghua Li, Jianxin Yang, Zhao Cao | Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fine-grained domain features, while these features are important in distinguishing the specific domain tasks from general tasks. We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics. Firstly, we propose the fashion symbols, a novel abstract fashion concept layer, to represent different fashion items and to generalize various kinds of fine-grained fashion features, making modelling fine-grained attributes more effective. Secondly, the attributes prompt method is proposed to make the model learn specific attributes of fashion items explicitly. We design proper prompt templates according to the format of fashion data. Comprehensive experiments are conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and FashionSAP gets SOTA performances for four popular fashion tasks. The ablation study also shows the proposed abstract fashion symbols, and the attribute prompt method enables the model to acquire fine-grained semantics in the fashion domain effectively. The obvious performance gains from FashionSAP provide a new baseline for future fashion task research. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_FashionSAP_Symbols_and_Attributes_Prompt_for_Fine-Grained_Fashion_Vision-Language_Pre-Training_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.05051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_FashionSAP_Symbols_and_Attributes_Prompt_for_Fine-Grained_Fashion_Vision-Language_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_FashionSAP_Symbols_and_Attributes_Prompt_for_Fine-Grained_Fashion_Vision-Language_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models | Minghua Liu, Yinhao Zhu, Hong Cai, Shizhong Han, Zhan Ling, Fatih Porikli, Hao Su | Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm. We also utilize multi-view 3D priors and few-shot prompt tuning to boost performance significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets shows that our method enables excellent zero-shot 3D part segmentation. Our few-shot version not only outperforms existing few-shot approaches by a large margin but also achieves highly competitive results compared to the fully supervised counterpart. Furthermore, we demonstrate that our method can be directly applied to iPhone-scanned point clouds without significant domain gaps. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_PartSLIP_Low-Shot_Part_Segmentation_for_3D_Point_Clouds_via_Pretrained_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_PartSLIP_Low-Shot_Part_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PartSLIP_Low-Shot_Part_Segmentation_for_3D_Point_Clouds_via_Pretrained_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PartSLIP_Low-Shot_Part_Segmentation_for_3D_Point_Clouds_via_Pretrained_CVPR_2023_paper.html | CVPR 2023 | null |
An Erudite Fine-Grained Visual Classification Model | Dongliang Chang, Yujun Tong, Ruoyi Du, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma | Current fine-grained visual classification (FGVC) models are isolated. In practice, we first need to identify the coarse-grained label of an object, then select the corresponding FGVC model for recognition. This hinders the application of the FGVC algorithm in real-life scenarios. In this paper, we propose an erudite FGVC model jointly trained by several different datasets, which can efficiently and accurately predict an object's fine-grained label across the combined label space. We found through a pilot study that positive and negative transfers co-occur when different datasets are mixed for training, i.e., the knowledge from other datasets is not always useful. Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets. In detail, we reduce negative transfer by decoupling the deep features through many dataset-specific feature extractors. Subsequently, these are channel-wise re-fused to facilitate positive transfer. Finally, we propose a meta-learning based dataset-agnostic spatial attention layer to take full advantage of the multi-dataset training data, given that localisation is dataset-agnostic between different datasets. Experimental results across 11 different mixed-datasets built on four different FGVC datasets demonstrate the effectiveness of the proposed method. Furthermore, the proposed method can be easily combined with existing FGVC methods to obtain state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_An_Erudite_Fine-Grained_Visual_Classification_Model_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_An_Erudite_Fine-Grained_Visual_Classification_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_An_Erudite_Fine-Grained_Visual_Classification_Model_CVPR_2023_paper.html | CVPR 2023 | null |
MAGVLT: Masked Generative Vision-and-Language Transformer | Sungwoong Kim, Daejin Jo, Donghoon Lee, Jongmin Kim | While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and text data by a single model rather than a generation of one fixed modality conditioned on the other modality. In this paper, we explore a unified generative vision-and-language (VL) model that can produce both images and text sequences. Especially, we propose a generative VL transformer based on the non-autoregressive mask prediction, named MAGVLT, and compare it with an autoregressive generative VL transformer (ARGVLT). In comparison to ARGVLT, the proposed MAGVLT enables bidirectional context encoding, fast decoding by parallel token predictions in an iterative refinement, and extended editing capabilities such as image and text infilling. For rigorous training of our MAGVLT with image-text pairs from scratch, we combine the image-to-text, text-to image, and joint image-and-text mask prediction tasks. Moreover, we devise two additional tasks based on the step-unrolled mask prediction and the selective prediction on the mixture of two image-text pairs. Experimental results on various downstream generation tasks of VL benchmarks show that our MAGVLT outperforms ARGVLT by a large margin even with significant inference speedup. Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderate-sized model (fewer than 500M parameters) even without the use of monomodal data and networks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_MAGVLT_Masked_Generative_Vision-and-Language_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_MAGVLT_Masked_Generative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_MAGVLT_Masked_Generative_Vision-and-Language_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_MAGVLT_Masked_Generative_Vision-and-Language_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Structure Aggregation for Cross-Spectral Stereo Image Guided Denoising | Zehua Sheng, Zhu Yu, Xiongwei Liu, Si-Yuan Cao, Yuqi Liu, Hui-Liang Shen, Huaqi Zhang | To obtain clean images with salient structures from noisy observations, a growing trend in current denoising studies is to seek the help of additional guidance images with high signal-to-noise ratios, which are often acquired in different spectral bands such as near infrared. Although previous guided denoising methods basically require the input images to be well-aligned, a more common way to capture the paired noisy target and guidance images is to exploit a stereo camera system. However, current studies on cross-spectral stereo matching cannot fully guarantee the pixel-level registration accuracy, and rarely consider the case of noise contamination. In this work, for the first time, we propose a guided denoising framework for cross-spectral stereo images. Instead of aligning the input images via conventional stereo matching, we aggregate structures from the guidance image to estimate a clean structure map for the noisy target image, which is then used to regress the final denoising result with a spatially variant linear representation model. Based on this, we design a neural network, called as SANet, to complete the entire guided denoising process. Experimental results show that, our SANet can effectively transfer structures from an unaligned guidance image to the restoration result, and outperforms state-of-the-art denoisers on various stereo image datasets. Besides, our structure aggregation strategy also shows its potential to handle other unaligned guided restoration tasks such as super-resolution and deblurring. The source code is available at https://github.com/lustrouselixir/SANet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sheng_Structure_Aggregation_for_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
Decoupling Human and Camera Motion From Videos in the Wild | Vickie Ye, Georgios Pavlakos, Jitendra Malik, Angjoo Kanazawa | We propose a method to reconstruct global human trajectories from videos in the wild. Our optimization method decouples the camera and human motion, which allows us to place people in the same world coordinate frame. Most existing methods do not model the camera motion; methods that rely on the background pixels to infer 3D human motion usually require a full scene reconstruction, which is often not possible for in-the-wild videos. However, even when existing SLAM systems cannot recover accurate scene reconstructions, the background pixel motion still provides enough signal to constrain the camera motion. We show that relative camera estimates along with data-driven human motion priors can resolve the scene scale ambiguity and recover global human trajectories. Our method robustly recovers the global 3D trajectories of people in challenging in-the-wild videos, such as PoseTrack. We quantify our improvement over existing methods on 3D human dataset Egobody. We further demonstrate that our recovered camera scale allows us to reason about motion of multiple people in a shared coordinate frame, which improves performance of downstream tracking in PoseTrack. Code and additional results can be found at https://vye16.github.io/slahmr/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_Decoupling_Human_and_Camera_Motion_From_Videos_in_the_Wild_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_Decoupling_Human_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.12827 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Decoupling_Human_and_Camera_Motion_From_Videos_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Decoupling_Human_and_Camera_Motion_From_Videos_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-Training via Word-Region Alignment | Lewei Yao, Jianhua Han, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Hang Xu | This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD). Unlike previous OVD frameworks that typically rely on a pre-trained vision-language model (e.g., CLIP) or exploit image-text pairs via a pseudo labeling process, DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. To accomplish this, we employ a maximum word-region similarity between region proposals and textual words to guide the contrastive objective. To enable the model to gain localization capability while learning broad concepts, DetCLIPv2 is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation. By jointly training with an alternating scheme and adopting low-resolution input for image-text pairs, DetCLIPv2 exploits image-text pair data efficiently and effectively: DetCLIPv2 utilizes 13x more image-text pairs than DetCLIP with a similar training time and improves performance. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance, e.g., DetCLIPv2 with Swin-T backbone achieves 40.4% zero-shot AP on the LVIS benchmark, which outperforms previous works GLIP/GLIPv2/DetCLIP by 14.4/11.4/4.5% AP, respectively, and even beats its fully-supervised counterpart by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_DetCLIPv2_Scalable_Open-Vocabulary_Object_Detection_Pre-Training_via_Word-Region_Alignment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_DetCLIPv2_Scalable_Open-Vocabulary_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04514 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_DetCLIPv2_Scalable_Open-Vocabulary_Object_Detection_Pre-Training_via_Word-Region_Alignment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_DetCLIPv2_Scalable_Open-Vocabulary_Object_Detection_Pre-Training_via_Word-Region_Alignment_CVPR_2023_paper.html | CVPR 2023 | null |
Adversarially Robust Neural Architecture Search for Graph Neural Networks | Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Rex Ying, Wenwu Zhu | Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an architectural perspective. Neural Architecture Search (NAS) has the potential to solve this problem by automating GNN architecture designs. Nevertheless, current graph NAS approaches lack robust design and are vulnerable to adversarial attacks. To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA). Specifically, we design a robust search space for the message-passing mechanism by adding graph structure mask operations into the search space, which comprises various defensive operation candidates and allows us to search for defensive GNNs. Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures. In this way, G-RNA helps understand GNN robustness from an architectural perspective and effectively searches for optimal adversarial robust GNNs. Extensive experimental results on benchmark datasets show that G-RNA significantly outperforms manually designed robust GNNs and vanilla graph NAS baselines by 12.1% to 23.4% under adversarial attacks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Adversarially_Robust_Neural_Architecture_Search_for_Graph_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Adversarially_Robust_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04168 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Adversarially_Robust_Neural_Architecture_Search_for_Graph_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Adversarially_Robust_Neural_Architecture_Search_for_Graph_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Affordance Grounding From Demonstration Video To Target Image | Joya Chen, Difei Gao, Kevin Qinghong Lin, Mike Zheng Shou | Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass view. The video-to-image affordance grounding task is challenging due to (1) the need to predict fine-grained affordances, and (2) the limited training data, which inadequately covers video-image discrepancies and negatively impacts grounding. To tackle them, we propose Affordance Transformer (Afformer), which has a fine-grained transformer-based decoder that gradually refines affordance grounding. Moreover, we introduce Mask Affordance Hand (MaskAHand), a self-supervised pretraining technique for synthesizing video-image data and simulating context changes, enhancing affordance grounding across video-image discrepancies. Afformer with MaskAHand pre-training achieves state-of-the-art performance on multiple benchmarks, including a substantial 37% improvement on the OPRA dataset. Code is made available at https://github.com/showlab/afformer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Affordance_Grounding_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14644 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.html | CVPR 2023 | null |
GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds | Zihui Zhang, Bo Yang, Bing Wang, Bo Li | We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing methods which primarily rely on a large amount of human annotations for training neural networks, we propose the first purely unsupervised method, called GrowSP, to successfully identify complex semantic classes for every point in 3D scenes, without needing any type of human labels or pretrained models. The key to our approach is to discover 3D semantic elements via progressive growing of superpoints. Our method consists of three major components, 1) the feature extractor to learn per-point features from input point clouds, 2) the superpoint constructor to progressively grow the sizes of superpoints, and 3) the semantic primitive clustering module to group superpoints into semantic elements for the final semantic segmentation. We extensively evaluate our method on multiple datasets, demonstrating superior performance over all unsupervised baselines and approaching the classic fully supervised PointNet. We hope our work could inspire more advanced methods for unsupervised 3D semantic learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_GrowSP_Unsupervised_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_GrowSP_Unsupervised_Semantic_Segmentation_of_3D_Point_Clouds_CVPR_2023_paper.html | CVPR 2023 | null |
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal Retrieval | Yanglin Feng, Hongyuan Zhu, Dezhong Peng, Xi Peng, Peng Hu | Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval becomes popular with a burst of 2D and 3D data. However, this problem is challenging given the heterogeneous structure and semantic discrepancies. Moreover, imperfect annotations are ubiquitous given the ambiguous 2D and 3D content, thus inevitably producing noisy labels to degrade the learning performance. To tackle the problem, this paper proposes a robust 2D-3D retrieval framework (RONO) to robustly learn from noisy multimodal data. Specifically, one novel Robust Discriminative Center Learning mechanism (RDCL) is proposed in RONO to adaptively distinguish clean and noisy samples for respectively providing them with positive and negative optimization directions, thus mitigating the negative impact of noisy labels. Besides, we present a Shared Space Consistency Learning mechanism (SSCL) to capture the intrinsic information inside the noisy data by minimizing the cross-modal and semantic discrepancy between common space and label space simultaneously. Comprehensive mathematical analyses are given to theoretically prove the noise tolerance of the proposed method. Furthermore, we conduct extensive experiments on four 3D-model multimodal datasets to verify the effectiveness of our method by comparing it with 15 state-of-the-art methods. Code is available at https://github.com/penghu-cs/RONO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_RONO_Robust_Discriminative_Learning_With_Noisy_Labels_for_2D-3D_Cross-Modal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_RONO_Robust_Discriminative_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_RONO_Robust_Discriminative_Learning_With_Noisy_Labels_for_2D-3D_Cross-Modal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_RONO_Robust_Discriminative_Learning_With_Noisy_Labels_for_2D-3D_Cross-Modal_CVPR_2023_paper.html | CVPR 2023 | null |
One-Stage 3D Whole-Body Mesh Recovery With Component Aware Transformer | Jing Lin, Ailing Zeng, Haoqian Wang, Lei Zhang, Yu Li | Whole-body mesh recovery aims to estimate the 3D human body, face, and hands parameters from a single image. It is challenging to perform this task with a single network due to resolution issues, i.e., the face and hands are usually located in extremely small regions. Existing works usually detect hands and faces, enlarge their resolution to feed in a specific network to predict the parameter, and finally fuse the results. While this copy-paste pipeline can capture the fine-grained details of the face and hands, the connections between different parts cannot be easily recovered in late fusion, leading to implausible 3D rotation and unnatural pose. In this work, we propose a one-stage pipeline for expressive whole-body mesh recovery, named OSX, without separate networks for each part. Specifically, we design a Component Aware Transformer (CAT) composed of a global body encoder and a local face/hand decoder. The encoder predicts the body parameters and provides a high-quality feature map for the decoder, which performs a feature-level upsample-crop scheme to extract high-resolution part-specific features and adopt keypoint-guided deformable attention to estimate hand and face precisely. The whole pipeline is simple yet effective without any manual post-processing and naturally avoids implausible prediction. Comprehensive experiments demonstrate the effectiveness of OSX. Lastly, we build a large-scale Upper-Body dataset (UBody) with high-quality 2D and 3D whole-body annotations. It contains persons with partially visible bodies in diverse real-life scenarios to bridge the gap between the basic task and downstream applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_One-Stage_3D_Whole-Body_Mesh_Recovery_With_Component_Aware_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_One-Stage_3D_Whole-Body_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_One-Stage_3D_Whole-Body_Mesh_Recovery_With_Component_Aware_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_One-Stage_3D_Whole-Body_Mesh_Recovery_With_Component_Aware_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers | Bin Ren, Yahui Liu, Yue Song, Wei Bi, Rita Cucchiara, Nicu Sebe, Wei Wang | Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack; 2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy; 3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://github.com/yhlleo/MJP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_Masked_Jigsaw_Puzzle_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.12551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation | Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, Xi Li | Recently, diffusion models have achieved great success in image synthesis. However, when it comes to the layout-to-image generation where an image often has a complex scene of multiple objects, how to make strong control over both the global layout map and each detailed object remains a challenging task. In this paper, we propose a diffusion model named LayoutDiffusion that can obtain higher generation quality and greater controllability than the previous works. To overcome the difficult multimodal fusion of image and layout, we propose to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form. Moreover, Layout Fusion Module (LFM) and Object-aware Cross Attention (OaCA) are proposed to model the relationship among multiple objects and designed to be object-aware and position-sensitive, allowing for precisely controlling the spatial related information. Extensive experiments show that our LayoutDiffusion outperforms the previous SOTA methods on FID, CAS by relatively 46.35%, 26.70% on COCO-stuff and 44.29%, 41.82% on VG. Code is available at https://github.com/ZGCTroy/LayoutDiffusion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_LayoutDiffusion_Controllable_Diffusion_Model_for_Layout-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_LayoutDiffusion_Controllable_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17189 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_LayoutDiffusion_Controllable_Diffusion_Model_for_Layout-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_LayoutDiffusion_Controllable_Diffusion_Model_for_Layout-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network | Xuan Shen, Yaohua Wang, Ming Lin, Yilun Huang, Hao Tang, Xiuyu Sun, Yanzhi Wang | The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challenging, requiring non-trivial prior knowledge of network design. To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way. In DeepMAD, a CNN network is modeled as an information processing system whose expressiveness and effectiveness can be analytically formulated by their structural parameters. Then a constrained mathematical programming (MP) problem is proposed to optimize these structural parameters. The MP problem can be easily solved by off-the-shelf MP solvers on CPUs with a small memory footprint. In addition, DeepMAD is a pure mathematical framework: no GPU or training data is required during network design. The superiority of DeepMAD is validated on multiple large-scale computer vision benchmark datasets. Notably on ImageNet-1k, only using conventional convolutional layers, DeepMAD achieves 0.7% and 1.5% higher top-1 accuracy than ConvNeXt and Swin on Tiny level, and 0.8% and 0.9% higher on Small level. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_DeepMAD_Mathematical_Architecture_Design_for_Deep_Convolutional_Neural_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_DeepMAD_Mathematical_Architecture_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02165 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_DeepMAD_Mathematical_Architecture_Design_for_Deep_Convolutional_Neural_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_DeepMAD_Mathematical_Architecture_Design_for_Deep_Convolutional_Neural_Network_CVPR_2023_paper.html | CVPR 2023 | null |
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and Correction | Yifan Li, Hu Han, Shiguang Shan, Xilin Chen | Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we propose a Dynamic Instance-specific Selection and Correction method (DISC) for learning from noisy labels (LNL). We first use a two-view-based backbone for image classification, obtaining confidence for each image from two views. Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data. Benefiting from the dynamic threshold strategy and two-view learning, we can effectively group each instance into one of the three subsets (i.e., clean, hard, and purified) based on the prediction consistency and discrepancy by two views at each epoch. Finally, we employ different regularization strategies to conquer subsets with different degrees of label noise, improving the whole network's robustness. Comprehensive evaluations on three controllable and four real-world LNL benchmarks show that our method outperforms the state-of-the-art (SOTA) methods to leverage useful information in noisy data while alleviating the pollution of label noise. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_DISC_Learning_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.html | CVPR 2023 | null |
BBDM: Image-to-Image Translation With Brownian Bridge Diffusion Models | Bo Li, Kaitao Xue, Bin Liu, Yu-Kun Lai | Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models(DM) have shown great potentials for high-quality image synthesis, and have gained competitive performance on the task of image-to-image translation. However, most of the existing diffusion models treat image-to-image translation as conditional generation processes, and suffer heavily from the gap between distinct domains. In this paper, a novel image-to-image translation method based on the Brownian Bridge Diffusion Model(BBDM) is proposed, which models image-to-image translation as a stochastic Brownian Bridge process, and learns the translation between two domains directly through the bidirectional diffusion process rather than a conditional generation process. To the best of our knowledge, it is the first work that proposes Brownian Bridge diffusion process for image-to-image translation. Experimental results on various benchmarks demonstrate that the proposed BBDM model achieves competitive performance through both visual inspection and measurable metrics. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_BBDM_Image-to-Image_Translation_With_Brownian_Bridge_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_BBDM_Image-to-Image_Translation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.07680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_BBDM_Image-to-Image_Translation_With_Brownian_Bridge_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_BBDM_Image-to-Image_Translation_With_Brownian_Bridge_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ConQueR: Query Contrast Voxel-DETR for 3D Object Detection | Benjin Zhu, Zhe Wang, Shaoshuai Shi, Hang Xu, Lanqing Hong, Hongsheng Li | Although DETR-based 3D detectors simplify the detection pipeline and achieve direct sparse predictions, their performance still lags behind dense detectors with post-processing for 3D object detection from point clouds. DETRs usually adopt a larger number of queries than GTs (e.g., 300 queries v.s. 40 objects in Waymo) in a scene, which inevitably incur many false positives during inference. In this paper, we propose a simple yet effective sparse 3D detector, named Query Contrast Voxel-DETR (ConQueR), to eliminate the challenging false positives, and achieve more accurate and sparser predictions. We observe that most false positives are highly overlapping in local regions, caused by the lack of explicit supervision to discriminate locally similar queries. We thus propose a Query Contrast mechanism to explicitly enhance queries towards their best-matched GTs over all unmatched query predictions. This is achieved by the construction of positive and negative GT-query pairs for each GT, and a contrastive loss to enhance positive GT-query pairs against negative ones based on feature similarities. ConQueR closes the gap of sparse and dense 3D detectors, and reduces 60% false positives. Our single-frame ConQueR achieves 71.6 mAPH/L2 on the challenging Waymo Open Dataset validation set, outperforming previous sota methods by over 2.0 mAPH/L2. Code: https://github.com/poodarchu/EFG. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_ConQueR_Query_Contrast_Voxel-DETR_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_ConQueR_Query_Contrast_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.07289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_ConQueR_Query_Contrast_Voxel-DETR_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_ConQueR_Query_Contrast_Voxel-DETR_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Probing Neural Representations of Scene Perception in a Hippocampally Dependent Task Using Artificial Neural Networks | Markus Frey, Christian F. Doeller, Caswell Barry | Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to inferior temporal cortex (IT). However, the ability of these networks to explain representations in higher cortical areas is relatively lacking and considerably less well researched. For example, DNNs have been less successful as a model of the egocentric to allocentric transformation embodied by circuits in retrosplenial and posterior parietal cortex. We describe a novel scene perception benchmark inspired by a hippocampal dependent task, designed to probe the ability of DNNs to transform scenes viewed from different egocentric perspectives. Using a network architecture inspired by the connectivity between temporal lobe structures and the hippocampus, we demonstrate that DNNs trained using a triplet loss can learn this task. Moreover, by enforcing a factorized latent space, we can split information propagation into "what" and "where" pathways, which we use to reconstruct the input. This allows us to beat the state-of-the-art for unsupervised object segmentation on the CATER and MOVi-A,B,C benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Frey_Probing_Neural_Representations_of_Scene_Perception_in_a_Hippocampally_Dependent_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Frey_Probing_Neural_Representations_of_Scene_Perception_in_a_Hippocampally_Dependent_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Frey_Probing_Neural_Representations_of_Scene_Perception_in_a_Hippocampally_Dependent_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Frey_Probing_Neural_Representations_of_Scene_Perception_in_a_Hippocampally_Dependent_CVPR_2023_paper.html | CVPR 2023 | null |
Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting | Su Wang, Chitwan Saharia, Ceslee Montgomery, Jordi Pont-Tuset, Shai Noy, Stefano Pellegrini, Yasumasa Onoe, Sarah Laszlo, David J. Fleet, Radu Soricut, Jason Baldridge, Mohammad Norouzi, Peter Anderson, William Chan | Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to the input text prompt, while consistent with the input image. We present Imagen Editor, a cascaded diffusion model, built by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by incorporating object detectors for proposing inpainting masks during training. In addition, text-guided image inpainting captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Imagen_Editor_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Multiview Point Cloud Registration With Reliable Pose Graph Initialization and History Reweighting | Haiping Wang, Yuan Liu, Zhen Dong, Yulan Guo, Yu-Shen Liu, Wenping Wang, Bisheng Yang | In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses. However, constructing a densely-connected graph is time-consuming and contains lots of outlier edges, which makes the subsequent IRLS struggle to find correct poses. To address the above problems, we first propose to use a neural network to estimate the overlap between scan pairs, which enables us to construct a sparse but reliable pose graph. Then, we design a novel history reweighting function in the IRLS scheme, which has strong robustness to outlier edges on the graph. In comparison with existing multiview registration methods, our method achieves 11% higher registration recall on the 3DMatch dataset and 13% lower registration errors on the ScanNet dataset while reducing 70% required pairwise registrations. Comprehensive ablation studies are conducted to demonstrate the effectiveness of our designs. The source code is available at https://github.com/WHU-USI3DV/SGHR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Robust_Multiview_Point_Cloud_Registration_With_Reliable_Pose_Graph_Initialization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Robust_Multiview_Point_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00467 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Robust_Multiview_Point_Cloud_Registration_With_Reliable_Pose_Graph_Initialization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Robust_Multiview_Point_Cloud_Registration_With_Reliable_Pose_Graph_Initialization_CVPR_2023_paper.html | CVPR 2023 | null |
A Probabilistic Framework for Lifelong Test-Time Adaptation | Dhanajit Brahma, Piyush Rai | Test-time adaptation (TTA) is the problem of updating a pre-trained source model at inference time given test input(s) from a different target domain. Most existing TTA approaches assume the setting in which the target domain is stationary, i.e., all the test inputs come from a single target domain. However, in many practical settings, the test input distribution might exhibit a lifelong/continual shift over time. Moreover, existing TTA approaches also lack the ability to provide reliable uncertainty estimates, which is crucial when distribution shifts occur between the source and target domain. To address these issues, we present PETAL (Probabilistic lifElong Test-time Adaptation with seLf-training prior), which solves lifelong TTA using a probabilistic approach, and naturally results in (1) a student-teacher framework, where the teacher model is an exponential moving average of the student model, and (2) regularizing the model updates at inference time using the source model as a regularizer. To prevent model drift in the lifelong/continual TTA setting, we also propose a data-driven parameter restoration technique which contributes to reducing the error accumulation and maintaining the knowledge of recent domains by restoring only the irrelevant parameters. In terms of predictive error rate as well as uncertainty based metrics such as Brier score and negative log-likelihood, our method achieves better results than the current state-of-the-art for online lifelong test-time adaptation across various benchmarks, such as CIFAR-10C, CIFAR-100C, ImageNetC, and ImageNet3DCC datasets. The source code for our approach is accessible at https://github.com/dhanajitb/petal. | https://openaccess.thecvf.com/content/CVPR2023/papers/Brahma_A_Probabilistic_Framework_for_Lifelong_Test-Time_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Brahma_A_Probabilistic_Framework_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.09713 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Brahma_A_Probabilistic_Framework_for_Lifelong_Test-Time_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Brahma_A_Probabilistic_Framework_for_Lifelong_Test-Time_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment | Kim Sung-Bin, Arda Senocak, Hyunwoo Ha, Andrew Owens, Tae-Hyun Oh | How does audio describe the world around us? In this paper, we propose a method for generating an image of a scene from sound. Our method addresses the challenges of dealing with the large gaps that often exist between sight and sound. We design a model that works by scheduling the learning procedure of each model component to associate audio-visual modalities despite their information gaps. The key idea is to enrich the audio features with visual information by learning to align audio to visual latent space. We translate the input audio to visual features, then use a pre-trained generator to produce an image. To further improve the quality of our generated images, we use sound source localization to select the audio-visual pairs that have strong cross-modal correlations. We obtain substantially better results on the VEGAS and VGGSound datasets than prior approaches. We also show that we can control our model's predictions by applying simple manipulations to the input waveform, or to the latent space. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sung-Bin_Sound_to_Visual_Scene_Generation_by_Audio-to-Visual_Latent_Alignment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sung-Bin_Sound_to_Visual_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.17490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sung-Bin_Sound_to_Visual_Scene_Generation_by_Audio-to-Visual_Latent_Alignment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sung-Bin_Sound_to_Visual_Scene_Generation_by_Audio-to-Visual_Latent_Alignment_CVPR_2023_paper.html | CVPR 2023 | null |
OSRT: Omnidirectional Image Super-Resolution With Distortion-Aware Transformer | Fanghua Yu, Xintao Wang, Mingdeng Cao, Gen Li, Ying Shan, Chao Dong | Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODI SR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_OSRT_Omnidirectional_Image_Super-Resolution_With_Distortion-Aware_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_OSRT_Omnidirectional_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.03453 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_OSRT_Omnidirectional_Image_Super-Resolution_With_Distortion-Aware_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_OSRT_Omnidirectional_Image_Super-Resolution_With_Distortion-Aware_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Text With Knowledge Graph Augmented Transformer for Video Captioning | Xin Gu, Guang Chen, Yufei Wang, Libo Zhang, Tiejian Luo, Longyin Wen | Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail and open set issues of words. In this paper, we propose a text with knowledge graph augmented transformer (TextKG) for video captioning. Notably, TextKG is a two-stream transformer, formed by the external stream and internal stream. The external stream is designed to absorb external knowledge, which models the interactions between the external knowledge, e.g., pre-built knowledge graph, and the built-in information of videos, e.g., the salient object regions, speech transcripts, and video captions, to mitigate the open set of words challenge. Meanwhile, the internal stream is designed to exploit the multi-modality information in original videos (e.g., the appearance of video frames, speech transcripts, and video captions) to deal with the long-tail issue. In addition, the cross attention mechanism is also used in both streams to share information. In this way, the two streams can help each other for more accurate results. Extensive experiments conducted on four challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions, MSR-VTT, and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Specifically, the proposed TextKG method outperforms the best published results by improving 18.7% absolute CIDEr scores on the YouCookII dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gu_Text_With_Knowledge_Graph_Augmented_Transformer_for_Video_Captioning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.12423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_Text_With_Knowledge_Graph_Augmented_Transformer_for_Video_Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_Text_With_Knowledge_Graph_Augmented_Transformer_for_Video_Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training | Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, Dhruv Mahajan | Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve the following three aspects of the contrastive pre-training pipeline: dataset noise, model initialization and the training objective. First, we propose a straightforward filtering strategy titled Complexity, Action, and Text-spotting (CAT) that significantly reduces dataset size, while achieving improved performance across zero-shot vision-language tasks. Next, we propose an approach titled Concept Distillation to leverage strong unimodal representations for contrastive training that does not increase training complexity while outperforming prior work. Finally, we modify the traditional contrastive alignment objective, and propose an importance-sampling approach to up-sample the importance of hard-negatives without adding additional complexity. On an extensive zero-shot benchmark of 29 tasks, our Distilled and Hard-negative Training (DiHT) approach improves on 20 tasks compared to the baseline. Furthermore, for few-shot linear probing, we propose a novel approach that bridges the gap between zero-shot and few-shot performance, substantially improving over prior work. Models are available at github.com/facebookresearch/diht. | https://openaccess.thecvf.com/content/CVPR2023/papers/Radenovic_Filtering_Distillation_and_Hard_Negatives_for_Vision-Language_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Radenovic_Filtering_Distillation_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02280 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Radenovic_Filtering_Distillation_and_Hard_Negatives_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Radenovic_Filtering_Distillation_and_Hard_Negatives_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
PointCMP: Contrastive Mask Prediction for Self-Supervised Learning on Point Cloud Videos | Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang, Yulan Guo, Qiong Liu, Xi Zhou | Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost. In this paper, we propose a contrastive mask prediction (PointCMP) framework for self-supervised learning on point cloud videos. Specifically, our PointCMP employs a two-branch structure to achieve simultaneous learning of both local and global spatio-temporal information. On top of this two-branch structure, a mutual similarity based augmentation module is developed to synthesize hard samples at the feature level. By masking dominant tokens and erasing principal channels, we generate hard samples to facilitate learning representations with better discrimination and generalization performance. Extensive experiments show that our PointCMP achieves the state-of-the-art performance on benchmark datasets and outperforms existing full-supervised counterparts. Transfer learning results demonstrate the superiority of the learned representations across different datasets and tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_PointCMP_Contrastive_Mask_Prediction_for_Self-Supervised_Learning_on_Point_Cloud_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.04075 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_PointCMP_Contrastive_Mask_Prediction_for_Self-Supervised_Learning_on_Point_Cloud_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_PointCMP_Contrastive_Mask_Prediction_for_Self-Supervised_Learning_on_Point_Cloud_CVPR_2023_paper.html | CVPR 2023 | null |
IS-GGT: Iterative Scene Graph Generation With Generative Transformers | Sanjoy Kundu, Sathyanarayanan N. Aakur | Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has proven useful in several tasks, such as question answering, captioning, and even object detection, to name a few. Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene, which adds computational overhead to the approach. This work introduces a generative transformer-based approach to generating scene graphs beyond link prediction. Using two transformer-based components, we first sample a possible scene graph structure from detected objects and their visual features. We then perform predicate classification on the sampled edges to generate the final scene graph. This approach allows us to efficiently generate scene graphs from images with minimal inference overhead. Extensive experiments on the Visual Genome dataset demonstrate the efficiency of the proposed approach. Without bells and whistles, we obtain, on average, 20.7% mean recall (mR@100) across different settings for scene graph generation (SGG), outperforming state-of-the-art SGG approaches while offering competitive performance to unbiased SGG approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kundu_IS-GGT_Iterative_Scene_Graph_Generation_With_Generative_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kundu_IS-GGT_Iterative_Scene_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kundu_IS-GGT_Iterative_Scene_Graph_Generation_With_Generative_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kundu_IS-GGT_Iterative_Scene_Graph_Generation_With_Generative_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Meta Omnium: A Benchmark for General-Purpose Learning-To-Learn | Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy Hospedales | Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction. This naturally raises the question of whether there is any few-shot meta-learning algorithm capable of generalizing across these diverse task types? To support the community in answering this question, we introduce Meta Omnium, a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. We experiment with popular few-shot meta-learning baselines and analyze their ability to generalize across tasks and to transfer knowledge between them. Meta Omnium enables meta-learning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bohdal_Meta_Omnium_A_Benchmark_for_General-Purpose_Learning-To-Learn_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bohdal_Meta_Omnium_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.07625 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bohdal_Meta_Omnium_A_Benchmark_for_General-Purpose_Learning-To-Learn_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bohdal_Meta_Omnium_A_Benchmark_for_General-Purpose_Learning-To-Learn_CVPR_2023_paper.html | CVPR 2023 | null |
Multimodal Industrial Anomaly Detection via Hybrid Fusion | Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, Chengjie Wang | 2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code at github.com/nomewang/M3DM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Multimodal_Industrial_Anomaly_Detection_via_Hybrid_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Multimodal_Industrial_Anomaly_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multimodal_Industrial_Anomaly_Detection_via_Hybrid_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multimodal_Industrial_Anomaly_Detection_via_Hybrid_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion | Wending Zhou, Xu Yan, Yinghong Liao, Yuankai Lin, Jin Huang, Gangming Zhao, Shuguang Cui, Zhen Li | Depth completion plays a crucial role in autonomous driving, in which cameras and LiDARs are two complementary sensors. Recent approaches attempt to exploit spatial geometric constraints hidden in LiDARs to enhance image-guided depth completion. However, only low efficiency and poor generalization can be achieved. In this paper, we propose BEV@DC, a more efficient and powerful multi-modal training scheme, to boost the performance of image-guided depth completion. In practice, the proposed BEV@DC model comprehensively takes advantage of LiDARs with rich geometric details in training, employing an enhanced depth completion manner in inference, which takes only images (RGB and depth) as input. Specifically, the geometric-aware LiDAR features are projected onto a unified BEV space, combining with RGB features to perform BEV completion. By equipping a newly proposed point-voxel spatial propagation network (PV-SPN), this auxiliary branch introduces strong guidance to the original image branches via 3D dense supervision and feature consistency. As a result, our baseline model demonstrates significant improvements with the sole image inputs. Concretely, it achieves state-of-the-art on several benchmarks, e.g., ranking Top-1 on the challenging KITTI depth completion benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_BEVDC_Birds-Eye_View_Assisted_Training_for_Depth_Completion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_BEVDC_Birds-Eye_View_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_BEVDC_Birds-Eye_View_Assisted_Training_for_Depth_Completion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_BEVDC_Birds-Eye_View_Assisted_Training_for_Depth_Completion_CVPR_2023_paper.html | CVPR 2023 | null |
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation | Tianheng Cheng, Xinggang Wang, Shaoyu Chen, Qian Zhang, Wenyu Liu | Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes. While, we find that box-supervised methods can produce some fine segmentation masks and we wonder whether the detectors could learn from these fine masks while ignoring low-quality masks. To answer this question, we present BoxTeacher, an efficient and end-to-end training framework for high-performance weakly supervised instance segmentation, which leverages a sophisticated teacher to generate high-quality masks as pseudo labels. Considering the massive noisy masks hurt the training, we present a mask-aware confidence score to estimate the quality of pseudo masks and propose the noise-aware pixel loss and noise-reduced affinity loss to adaptively optimize the student with pseudo masks. Extensive experiments can demonstrate the effectiveness of the proposed BoxTeacher. Without bells and whistles, BoxTeacher remarkably achieves 35.0 mask AP and 36.5 mask AP with ResNet-50 and ResNet-101 respectively on the challenging COCO dataset, which outperforms the previous state-of-the-art methods by a significant margin and bridges the gap between box-supervised and mask-supervised methods. The code and models will be available later. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_BoxTeacher_Exploring_High-Quality_Pseudo_Labels_for_Weakly_Supervised_Instance_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2210.05174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_BoxTeacher_Exploring_High-Quality_Pseudo_Labels_for_Weakly_Supervised_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_BoxTeacher_Exploring_High-Quality_Pseudo_Labels_for_Weakly_Supervised_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
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