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Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Leverage Your Local and Global Representations: A New Self-S...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Tong Qiu, Congpei Ke, Wei Suesstrunk, Sabine Salzmann, Mathieu Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lausanne Switzerland Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain different image information, e.g., background and small objects, and thus tends to restrain the diversity of the learned representations. In this work, we address this issue by introducing a new self-supervised learning strategy, LoGo, that explicitly reasons about Local and Global crops. To achieve view invariance, LoGo encourages similarity between global crops from the same image, as well as between a global and a local crop. However, to correctly encode the fact that the content of smaller crops may differ entirely, LoGo promotes two local crops to have dissimilar representations, while being close to global crops. Our LoGo strategy can easily be applied to existing SSL methods. Our extensive experiments on a variety of datasets and using different self-supervised learning frameworks validate its superiority over existing approaches. Noticeably, we achieve better results than supervised models on transfer learning when using only 1/10 of the data.

关键词： Representation learning computer vision Codes Computational modeling Crops Self-supervised learning Feature extraction

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Generalizing Face Forgery Detection with High-frequency Features

Generalizing Face Forgery Detection with High-frequency Feat...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Luo, Yuchen Zhang, Yong Yan, Junchi Liu, Wei Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence AI Inst Shanghai Peoples R China Tencent AI Lab Bellevue WA USA Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.

关键词： Training Correlation Image color analysis Databases Face recognition Detectors Feature extraction

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Spatiotemporal Fusion in 3D CNNs: A Probabilistic View

Spatiotemporal Fusion in 3D CNNs: A Probabilistic View

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Yizhou Sun, Xiaoyan Luo, Chong Zha, Zheng-Jun Zeng, Wenjun Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781728171685

Despite the success in still image recognition, deep neural networks for spatiotemporal signal tasks (such as human action recognition in videos) still suffers from low efficacy and inefficiency over the past years. Recently, human experts have put more efforts into analyzing the importance of different components in 3D convolutional neural networks (3D CNNs) to design more powerful spatiotemporal learning backbones. Among many others, spatiotemporal fusion is one of the essentials. It controls how spatial and temporal signals are extracted at each layer during inference. Previous attempts usually start by ad-hoc designs that empirically combine certain convolutions and then draw conclusions based on the performance obtained by training the corresponding networks. These methods only support network-level analysis on limited number of fusion strategies. In this paper, we propose to convert the spatiotemporal fusion strategies into a probability space, which allows us to perform network-level evaluations of various fusion strategies without having to train them separately. Besides, we can also obtain fine-grained numerical information such as layer-level preference on spatiotemporal fusion within the probability space. Our approach greatly boosts the efficiency of analyzing spatiotemporal fusion. Based on the probability space, we further generate new fusion strategies which achieve the state-of-the-art performance on four well-known action recognition datasets.

关键词： Image recognition

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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

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Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection

Democracy Does Matter: Comprehensive Feature Mining for Co-S...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Siyue Xiao, Jimin Zhang, Bingfeng Lim, Eng Gee XJTLU Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we aim to mine comprehensive co-salient features with democracy and reduce background interference without introducing any extra information. To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects. Then a comprehensive prototype based on the response maps can be generated as a guide for final prediction. To suppress the noisy background information in the prototype, we propose a self-contrastive learning module, where both positive and negative pairs are formed without relying on additional classification information. Besides, we also design a democratic feature enhancement module to further strengthen the co-salient features by readjusting attention values. Extensive experiments show that our model obtains better performance than previous state-of-the-art methods, especially on challenging real-world cases (e.g., for CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for maximum E-measure, and 3.7% for S-measure) under the same settings. Source code is available at https://***/siyueyu/DCFM.

关键词： computer vision Codes Aggregates Prototypes Object detection Interference Feature extraction

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GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting

GPV-Pose: Category-level Object Pose Estimation via Geometry...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Di, Yan Zhang, Ruida Lou, Zhiqiang Manhardt, Fabian Ji, Xiangyang Navab, Nassir Tombari, Federico Tech Univ Munich Munich Germany Tsinghua Univ Beijing Peoples R China Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications. To circumvent this problem, category-level object pose estimation has recently been revamped, which aims at predicting the 6D pose as well as the 3D metric size for previously unseen instances from a given set of object classes. This is, however, a much more challenging task due to severe intra-class shape variations. To address this issue, we propose GPV-Pose, a novel framework for robust category-level pose estimation, harnessing geometric insights to enhance the learning of category-level pose-sensitive features. First, we introduce a decoupled confidence-driven rotation representation, which allows geometry-aware recovery of the associated rotation matrix. Second, we propose a novel geometry-guided point-wise voting paradigm for robust retrieval of the 3D object bounding box. Finally, leveraging these different output streams, we can enforce several geometric consistency terms, further increasing performance, especially for non-symmetric categories. GPV-Pose produces superior results to state-of-the-art competitors on common public benchmarks, whilst almost achieving real-time inference speed at 20 FPS.

关键词： Point cloud compression Measurement computer vision Three-dimensional displays Shape Pose estimation Real-time systems

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A stroke of genius: Predicting the next move in badminton

A stroke of genius: Predicting the next move in badminton

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ibh, Magnus Grasshof, Stella Hansen, Dan Witzner IT Univ Copenhagen Machine Learning Grp Copenhagen Denmark

ISBN: (纸本)9798350365474

This paper presents, RallyTemPose, a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position, skeleton poses, and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks, which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion, the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons. The code can be found at: This https url.

关键词： Action Forecasting computer vision Encoder-Decoder Skeleton-data Sports Application

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Neural RGB-D Surface Reconstruction

Neural RGB-D Surface Reconstruction

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Azinovic, Dejan Martin-Brualla, Ricardo Goldman, Dan B. Niessner, Matthias Thies, Justus Tech Univ Munich Munich Germany Google Res Mountain View CA USA Max Planck Inst Intelligent Syst Stuttgart Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications. While current volume-based view synthesis methods that use neural radiance fields (NeRFs) show promising results in reproducing the appearance of an object or scene, they do not reconstruct an actual surface. The volumetric representation of the surface based on densities leads to artifacts when a surface is extracted using Marching Cubes, since during optimization, densities are accumulated along the ray and are not used at a single sample point in isolation. Instead of this volumetric representation of the surface, we propose to represent the surface using an implicit function (truncated signed distance function). We show how to incorporate this representation in the NeRF framework, and extend it to use depth measurements from a commodity RGB-D sensor;such as a Kinect. In addition, we propose a pose and camera refinement technique which improves the overall reconstruction quality. In contrast to concurrent work on integrating depth priors in NeRF which concentrates on novel view synthesis, our approach is able to reconstruct high-quality, metrical 3D reconstructions.

关键词： Surface reconstruction Teleconferencing Planing computer vision Three-dimensional displays Current measurement Mixed reality

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Automated Progressive Learning for Efficient Training of vision Transformers

Automated Progressive Learning for Efficient Training of Vis...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Changlin Zhuang, Bohan Wang, Guangrun Liang, Xiaodan Chang, Xiaojun Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney AAII ReLER Sydney NSW Australia Monash Univ Clayton Vic Australia Univ Oxford Oxford England Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, highlighting the urgent need to develop efficient training methods for ViTs. Progressive learning, a training scheme where the model capacity grows progressively during training, has started showing its ability in efficient training. In this paper, we take a practical step towards efficient training of ViTs by customizing and automating progressive learning. First, we develop a strong manual baseline for progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge the gap brought by model growth. Then, we propose automated progressive learning (AutoProg), an efficient training scheme that aims to achieve lossless acceleration by automatically increasing the training overload on-the-fly;this is achieved by adaptively deciding whether, where and how much should the model grow during progressive learning. Specifically, we first relax the optimization of the growth schedule to sub-network architecture optimization problem, then propose one-shot estimation of the sub-network performance via an elastic supernet. The searching overhead is reduced to minimal by recycling the parameters of the supernet. Extensive experiments of efficient training on ImageNet with two representative ViT models, DeiT and VOLO, demonstrate that AutoProg can accelerate ViTs training by up to 85.1% with no performance drop.(1)

关键词： Training Adaptation models Schedules Computational modeling Estimation Manuals Transformers

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Scene Representation in Bird's-Eye View from Surrounding Cameras with Transformers

Scene Representation in Bird's-Eye View from Surrounding Cam...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Yun Zhang, Yu Gong, Zhan Zhu, Hong Inspur Elect Informat Ind Co Ltd Dept AI & HPC Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Scene representation in the bird's-eye-view (BEV) coordinate frame provides a succinct and effective way to understand surrounding environments for autonomous vehicles and robotics. In this work, we present an end-to-end architecture to generate the BEV representation from surrounding cameras. To generate the BEV representation, we propose a transformer-based encoder-decoder structure to translate the image features from different cameras into the BEV frame, which takes advantage of the context information in the individual image and the relationship between images in different views. We perform multiple semantic segmentation tasks using the BEV features. Experimental results show that our model outperforms the competitive baseline [20], which demonstrates the effectiveness and efficiency of our method.

关键词： Image segmentation Image coding Computational modeling Semantics Robot vision systems Predictive models Cameras

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