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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
30976 条 记 录,以下是4861-4870 订阅
排序:
Unsupervised Learning of Debiased Representations with Pseudo-Attributes
Unsupervised Learning of Debiased Representations with Pseud...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Seo, Seonguk Lee, Joon-Young Han, Bohyung Seoul Natl Univ ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAL Seoul South Korea Adobe Res San Jose CA USA
Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations. Although existing works often handl... 详细信息
来源: 评论
Masked-attention Mask Transformer for Universal Image Segmentation
Masked-attention Mask Transformer for Universal Image Segmen...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Cheng, Bowen Misra, Ishan Schwing, Alexander G. Kirillov, Alexander Girdhar, Rohit Facebook AI Res FAIR Menlo Pk CA 94025 USA Univ Illinois Urbana Champaign UIUC Champaign IL 61820 USA
Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on desi... 详细信息
来源: 评论
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations  30
Efficient Diffusion on Region Manifolds: Recovering Small Ob...
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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Iscen, Ahmet Tolias, Giorgos Avrithis, Yannis Furon, Teddy Chum, Ondrej Inria Rennes Rennes France CTU FEE VRG Prague Czech Republic
Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a me... 详细信息
来源: 评论
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neu...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ma, Ruichen Qiao, Guanchao Liu, Yian Meng, Liwei Ning, Ning Liu, Yang Hu, Shaogang Univ Elect Sci & Technol China Chengdu Peoples R China
Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inef... 详细信息
来源: 评论
GLID: Pre-training a Generalist Encoder-Decoder vision Model
GLID: Pre-training a Generalist Encoder-Decoder Vision Model
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Jihao Zheng, Jinliang Liu, Yu Li, Hongsheng CUHK MMLab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China CPII InnoHK Hong Kong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Shanghai Peoples R China
This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches, e.g., Masked Autoencoder, have s... 详细信息
来源: 评论
Deep-BCN: Deep networks meet biased competition to create a brain-inspired model of attention control  31
Deep-BCN: Deep networks meet biased competition to create a ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Adeli, Hossein Zelinsky, Gregory SUNY Stony Brook Stony Brook NY 11794 USA
The mechanism of attention control is best described by biased-competition theory (BCT), which suggests that a top-down goal state biases a competition among object representations for the selective routing of a visua... 详细信息
来源: 评论
What Should Be Equivariant In Self-Supervised Learning
What Should Be Equivariant In Self-Supervised Learning
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Xie, Yuyang Wen, Jianhong Lau, Kin Wai Rehman, Yasar Abbas Ur Shen, Jiajun TCL AI Lab Hong Kong Peoples R China Fuzhou Univ Fuzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China
Self-supervised learning (SSL) aims to learn feature representation without human-annotated data. Existing methods approach this goal by encouraging the feature representations to be invariant under a set of task-irre... 详细信息
来源: 评论
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
Cross-MPI: Cross-scale Stereo for Image Super-Resolution usi...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhou, Yuemei Wu, Gaochang Fu, Ying Li, Kun Liu, Yebin Tsinghua Univ Beijing Peoples R China ZhuoHe Tech Beijing Peoples R China Northeastern Univ Boston MA 02115 USA Beijing Inst Technol Beijing Peoples R China Tianjin Univ Tianjin Peoples R China
Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems. However, existing RefSR approaches fail to acc... 详细信息
来源: 评论
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhou, Qunjie Sattler, Torsten Leal-Taixe, Laura Tech Univ Munich Munich Germany Czech Tech Univ CIIRC Prague Czech Republic
The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. Recently emerged correspond... 详细信息
来源: 评论
Learning to Segment the Tail
Learning to Segment the Tail
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Hu, Xinting Jiang, Yi Tang, Kaihua Chen, Jingyuan Miao, Chunyan Zhang, Hanwang Nanyang Technol Univ Singapore Singapore Alibaba Grp Hangzhou Zhejiang Peoples R China Alibaba Grp Damo Acad Hangzhou Zhejiang Peoples R China
Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data. We propose a "divide&conquer" strategy for the challenging LVIS task: divide the whole data i... 详细信息
来源: 评论