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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4901-4910 订阅

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Exploring Dual-task Correlation for Pose Guided Person Image Generation

Exploring Dual-task Correlation for Pose Guided Person Image...

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

作者： Zhang, Pengze Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose. Most of the existing methods only focus on the ill-posed source-to-target task and fail to capture reasonable texture mapping. To address this problem, we propose a novel Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task (i.e., source-to-source task) and exploits the dual-task correlation to promote the performance of PGPIG. The DPTN is of a Siamese structure, containing a source-to-source self-reconstruction branch, and a transformation branch for source-to-target generation. By sharing partial weights between them, the knowledge learned by the source-to-source task can effectively assist the source-to-target learning. Furthermore, we bridge the two branches with a proposed Pose Transformer Module (PTM) to adaptively explore the correlation between features from dual tasks. Such correlation can establish the fine-grained mapping of all the pixels between the sources and the targets, and promote the source texture transmission to enhance the details of the generated target images. Extensive experiments show that our DPTN outperforms state-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN only contains 9.79 million parameters, which is significantly smaller than other approaches. Our code is available at: https://***/PangzeCheung/Dual-task-PoseTransformer-Network.

关键词： computer vision Correlation Codes Image synthesis Computational modeling Focusing computer architecture

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ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

ARVo: Learning All-Range Volumetric Correspondence for Video...

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

作者： Li, Dongxu Xu, Chenchen Zhang, Kaihao Yu, Xin Zhong, Yiran Ren, Wenqi Suominen, Hanna Li, Hongdong Australian Natl Univ Canberra ACT Australia DATA61 CSIRO Sydney NSW Australia UTS Sydney NSW Australia Chinese Acad Sci IIE Beijing Peoples R China Univ Turku Turku Finland

ISBN: (纸本)9781665445092

Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.

关键词： Training Solid modeling computer vision Correlation DVD Computational modeling Cameras

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Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation 32

Distant Supervised Centroid Shift: A Simple and Efficient Ap...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liang, Jian He, Ran Sun, Zhenan Tan, Tieniu Chinese Acad Sci CRIPAC Inst Automat Beijing Peoples R China Chinese Acad Sci NLPR Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9781728132938

Conventional domain adaptation methods usually resort to deep neural networks or subspace learning to find invariant representations across domains. However, most deep learning methods highly rely on large-size source domains and are computationally expensive to train, while subspace learning methods always have a quadratic time complexity that suffers from the large domain size. This paper provides a simple and efficient solution, which could be regarded as a well-performing baseline for domain adaptation tasks. Our method is built upon the nearest centroid classifier, seeking a subspace where the centroids in the target domain are moderately shifted from those in the source domain. Specifically, we design a unified objective without accessing the source domain data and adopt an alternating minimization scheme to iteratively discover the pseudo target labels, invariant subspace, and target centroids. Besides its privacy-preserving property (distant supervision), the algorithm is provably convergent and has a promising linear time complexity. In addition, the proposed method can be readily extended to multi-source setting and domain generalization, and it remarkably enhances popular deep adaptation methods by borrowing the learned transferable features. Extensive experiments on several benchmarks including object, digit, and face recognition datasets validate that our methods yield state-of-the-art results in various domain adaptation tasks.

关键词： Categorization recognition: Detection Retrieval Statistical Learning

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An Empirical Study of Training End-to-End vision-and-Language Transformers

An Empirical Study of Training End-to-End Vision-and-Languag...

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

作者： Dou, Zi-Yi Xu, Yichong Gan, Zhe Wang, Jianfeng Wang, Shuohang Wang, Lijuan Zhu, Chenguang Zhang, Pengchuan Yuan, Lu Peng, Nanyun Liu, Zicheng Zeng, Michael Univ Calif Los Angeles Los Angeles CA 90024 USA Microsoft Corp Redmond WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision-and-language (VL) pre-training has proven to be highly effective on various VL downstream tasks. While recent work has shown that fully transformer-based VL models can be more efficient than previous region-feature-based methods, their performance on downstream tasks often degrades significantly. In this paper, we present Meter, a Multimodal End-to-end TransformER framework, through which we investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner. Specifically, we dissect the model designs along multiple dimensions: vision encoders (e.g., CLIP-ViT, Swin transformer), text encoders (e.g., RoBERTa, De-BERTa), multimodal fusion module (e.g., merged attention vs. co-attention), architectural design (e.g., encoder-only vs. encoder-decoder), and pre-training objectives (e.g., masked image modeling). We conduct comprehensive experiments and provide insights on how to train a performant VL transformer. Meterachieves an accuracy of 77.64% on the VQAv2 test-std set using only 4M images for pre-training, surpassing the state-of-the-art region-feature-based model by 1.04%, and outperforming the previous best fully transformer-based model by 1.6%. Notably, when further scaled up, our best VQA model achieves an accuracy of 80.54%. Code and pre-trained models are released at https://***/zdou0830/METER.

关键词： Meters Training computer vision Codes Computational modeling Transformers pattern recognition

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Exclusivity-Consistency Regularized Multi-view Subspace Clustering 30

Exclusivity-Consistency Regularized Multi-view Subspace Clus...

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xiaobo Guo, Xiaojie Lei, Zhen Zhang, Changqing Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci IIE State Key Lab Informat Secur Beijing Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781538604571

Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering them simultaneously. In this paper, we propose a novel multi-view subspace clustering model that attempts to harness the complementary information between different representations by introducing a novel position-aware exclusivity term. Meanwhile, a consistency term is employed to make these complementary representations to further have a common indicator. We formulate the above concerns into a unified optimization framework. Experimental results on several benchmark datasets are conducted to reveal the effectiveness of our algorithm over other state-of-the-arts.

关键词： Clustering algorithms Optimization pattern recognition computer vision Benchmark testing Standards conferences

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Attenuating Catastrophic Forgetting by Joint Contrastive and Incremental Learning

Attenuating Catastrophic Forgetting by Joint Contrastive and...

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

作者： Ferdinand, Quentin Clement, Benoit Oliveau, Quentin Le Chenadec, Gilles Papadakis, Panagiotis Naval Grp Res Cherbourg En Cotentin France ENSTA Bretagne Lab STICC UMR 6285 Brest France IMT Atlantique Lab STICC UMR 6285 Brest France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In class incremental learning, discriminative models are trained to classify images while adapting to new instances and classes incrementally. Training a model to adapt to new classes without total access to previous class data, however, leads to the known problem of catastrophic forgetting of the previously learnt classes. To alleviate this problem, we show how we can build upon recent progress on contrastive learning methods. In particular, we develop an incremental learning approach for deep neural networks operating both at classification and representation level which alleviates forgetting and learns more general features for data classification. Experiments performed on several datasets demonstrate the superiority of the proposed method with respect to well known state-of-the-art methods.

关键词： Learning systems Training Deep learning Adaptation models computer vision conferences Computational modeling

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Duplex Generative Adversarial Network for Unsupervised Domain Adaptation 31

Duplex Generative Adversarial Network for Unsupervised Domai...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Lanqing Kan, Meina Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Tech Beijing Peoples R China

ISBN: (纸本)9781538664209

Domain adaptation attempts to transfer the knowledge obtained from the source domain to the target domain, i.e., the domain where the testing data are. The main challenge lies in the distribution discrepancy between source and target domain. Most existing works endeavor to learn domain invariant representation usually by minimizing a distribution distance, e.g., MMD and the discriminator in the recently proposed generative adversarial network (GAN). Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation. Specifically, our proposed network consists of three parts, an encoder, a generator and two discriminators. The encoder embeds samples from both domains into the latent representation, and the generator decodes the latent representation to both source and target domains respectively conditioned on a domain code, i.e., achieves domain transformation. The generator is pitted against duplex discriminators, one for source domain and the other for target, to ensure the reality of domain transformation, the latent representation domain invariant and the category information of it preserved as well. Our proposed work achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.

关键词： Generators Generative adversarial networks Gallium nitride Measurement Feature extraction Testing Object recognition

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Learning a Self-Expressive Network for Subspace Clustering

Learning a Self-Expressive Network for Subspace Clustering

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

作者： Zhang, Shangzhi You, Chong Vidal, Rene Li, Chun-Guang Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Johns Hopkins Univ Math Inst Data Sci Baltimore MD USA

ISBN: (纸本)9781665445092

State-of-the-art subspace clustering methods are based on the self-expressive model, which represents each data point as a linear combination of other data points. However, such methods are designed for a finite sample dataset and lack the ability to generalize to out-of-sample data. Moreover, since the number of self-expressive coefficients grows quadratically with the number of data points, their ability to handle large-scale datasets is often limited. In this paper, we propose a novel framework for subspace clustering, termed Self-Expressive Network (SENet), which employs a properly designed neural network to learn a self-expressive representation of the data. We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data. Besides, we show that SENet can also be leveraged to perform subspace clustering on large-scale datasets. Extensive experiments conducted on synthetic data and real world benchmark data validate the effectiveness of the proposed method. In particular, SENet yields highly competitive performance on MNIST, Fashion MNIST and Extended MNIST and state-of-the-art performance on CIFAR-10.

关键词： Training computer vision Computational modeling Clustering methods Neural networks Training data Stochastic processes

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Fine-grained Image Classification via Combining vision and Language 30

Fine-grained Image Classification via Combining Vision and L...

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Xiangteng Peng, Yuxin Peking Univ Inst Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781538604571

Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combining vision and language (CVL) for learning latent semantic representations. The vision stream learns deep representations from the original visual information via deep convolutional neural network. The language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combining the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.

关键词： Visualization Streaming media Natural languages Semantics Feature extraction Image recognition

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CD²-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

CD<SUP>2</SUP>-pFed: Cyclic Distillation-guided Channel Deco...

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

作者： Shen, Yiqing Zhou, Yuyin Yu, Lequan Shanghai Jiao Tong Univ Shanghai Peoples R China UC Santa Cruz Santa Cruz CA USA Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains challenging to deal with heterogeneous data clients, as the discrepant data distributions usually prevent the global model from delivering good generalization ability on each participating client. In this paper, we propose CD2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. Different from previous works which establish layer-wise personalization to overcome the non-HD data across different clients, we make the first attempt at channel-wise assignment for model personalization, referred to as channel decoupling. To further facilitate the collaboration between private and shared weights, we propose a novel cyclic distillation scheme to impose a consistent regularization between the local and global model representations during the federation. Guided by the cyclical distillation, our channel decoupling framework can deliver more accurate and generalized results for different kinds of heterogeneity, such as feature skew, label distribution skew, and concept shift. Comprehensive experiments on four benchmarks, including natural image and medical image analysis tasks, demonstrate the consistent effectiveness of our method on both local and external validations.

关键词： Privacy computer vision Image analysis Distance learning Microscopy Collaborative work Data models

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