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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11891 条记录，以下是901-910 订阅

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Learning Sample Relationship for Exposure Correction

Learning Sample Relationship for Exposure Correction

引用

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

作者： Huang, Jie Zhao, Feng Zhou, Man Xiao, Jie Zheng, Naishan Zheng, Kaiwen Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a mini-batch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design.

关键词： Low-level vision

来源：评论

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Referring Multi-Object Tracking

Referring Multi-Object Tracking

引用

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

作者： Wu, Dongming Han, Wencheng Wang, Tiancai Dong, Xingping Zhang, Xiangyu Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Univ Macau SKL IOTSC CIS Macau Peoples R China MEGVII Technol Beijing Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Beijing Acad Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The Refer-KITTI dataset and the code are released at https://***.

关键词： and reasoning language vision

来源：评论

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Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution

Equivalent Transformation and Dual Stream Network Constructi...

引用

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

作者： Chao, Jiahao Zhou, Zhou Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Dehbi, Lydia East China Normal Univ Shanghai Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Beijing Peoples R China

ISBN: (纸本)9798350301298

In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices. To address this issue, many lightweight super-resolution models have been proposed. However, these models still contain time-consuming components that increase inference latency, limiting their real-world applications on mobile devices. In this paper, we propose a novel model for single-image super-resolution based on Equivalent Transformation and Dual Stream network construction (ETDS). ET method is proposed to transform time-consuming operators into time-friendly operations, such as convolution and ReLU, on mobile devices. Then, a dual stream network is designed to alleviate redundant parameters resulting from the use of ET and enhance the feature extraction ability. Taking full advantage of the advance of ET and the dual stream network structure, we develop the efficient SR model ETDS for mobile devices. The experimental results demonstrate that our ETDS achieves superior inference speed and reconstruction quality compared to previous lightweight SR methods on mobile devices. The code is available at https://***/ECNUSR/ETDS.

关键词： Low-level vision

来源：评论

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Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face recognition

Discrete Point-wise Attack Is Not Enough: Generalized Manifo...

引用

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

作者： Li, Qian Hu, Yuxiao Liu, Ye Zhang, Dongxiao Jin, Xin Chen, Yuntian Eastern Inst Adv Study Ningbo Zhejiang Peoples R China

ISBN: (纸本)9798350301298

Classical adversarial attacks for Face recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA)(1) to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual quality.

关键词： Adversarial attack and defense

来源：评论

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GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for vision-and-Language Navigation

GeoVLN: Learning Geometry-Enhanced Visual Representation wit...

引用

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

作者： Huo, Jingyang Sun, Qiang Jiang, Boyan Lin, Haitao Fu, Yanwei Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.

关键词： Multi-modal learning

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Learning Rotation-Equivariant Features for Visual Correspondence

Learning Rotation-Equivariant Features for Visual Correspond...

引用

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

作者： Lee, Jongmin Kim, Byungjin Kim, Seungwook Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9798350301298

Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation.

关键词： Low-level vision

来源：评论

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Zero-shot Referring Image Segmentation with Global-Local Context Features

Zero-shot Referring Image Segmentation with Global-Local Con...

引用

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

作者： Yu, Seonghoon Seo, Paul Hongsuck Son, Jeany GIST AI Grad Sch Gwangju South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed instance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://***/Seonghoon-Yu/Zero-shot-RIS.

关键词： language reasoning vision

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NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for vision Transformers

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Qua...

引用

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

作者： Liu, Yijiang Yang, Huanrui Dong, Zhen Keutzer, Kurt Du, Li Zhang, Shanghang Nanjing Univ Nanjing Peoples R China Univ Calif Berkeley Berkeley CA USA Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China

ISBN: (纸本)9798350301298

The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead of tuning the quantizer to better fit the complicated activation distribution, this paper proposes NoisyQuant, a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. We make a surprising theoretical discovery that for a given quantizer, adding a fixed Uniform noisy bias to the values being quantized can significantly reduce the quantization error under provable conditions. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer. Extensive experiments show NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead. For instance, on linear uniform 6-bit activation quantization, NoisyQuant improves SOTA top-1 accuracy on ImageNet by up to 1.7%, 1.1% and 0.5% for ViT, DeiT, and Swin Transformer respectively, achieving on-par or even higher performance than previous nonlinear, mixed-precision quantization.

关键词： Efficient and scalable vision

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Re-thinking Federated Active Learning based on Inter-class Diversity

Re-thinking Federated Active Learning based on Inter-class D...

引用

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

作者： Kim, SangMook Bae, Sangmin Song, Hwanjun Yun, Se -Young KAIST AI Daejeon South Korea NAVER AI LAB Grenoble France

ISBN: (纸本)9798350301298

Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local interclass diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings. The code is available at: https://***/raymin0223/LoGo.

关键词： vision applications and systems

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TINC: Tree-structured Implicit Neural Compression

TINC: Tree-structured Implicit Neural Compression

引用

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

作者： Yang, Runzhao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://***/RichealYoung/TINC.

关键词： cell microscopy Medical and biological vision

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