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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition"
23241 条 记 录,以下是4941-4950 订阅
Flow Guided Transformable Bottleneck Networks for Motion Retargeting
Flow Guided Transformable Bottleneck Networks for Motion Ret...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ren, Jian Chai, Menglei Woodford, Oliver J. Olszewski, Kyle Tulyakov, Sergey Snap Inc Santa Monica CA 90405 USA
Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person. Existing efforts leverage a long training video from each target person to train a ... 详细信息
来源: 评论
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
DivCo: Diverse Conditional Image Synthesis via Contrastive G...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liu, Rui Ge, Yixiao Choi, Ching Lam Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China NVIDIA NVIDIA AI Technol Ctr Hong Kong Peoples R China Xidian Univ Sch CST Xian Peoples R China
Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. To solve t... 详细信息
来源: 评论
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dy...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Yao, Lewei Pi, Renjie Xu, Hang Zhang, Wei Li, Zhenguo Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China
We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques... 详细信息
来源: 评论
Proceedings - 2022 ieee/cvf Winter conference on Applications of computer vision, WACV 2022
Proceedings - 2022 IEEE/CVF Winter Conference on Application...
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22nd ieee/cvf Winter conference on Applications of computer vision, WACV 2022
The proceedings contain 406 papers. The topics discussed include: evaluation of correctness in unsupervised many-to-many image translation;fast and explicit neural view synthesis;training a task-specific image reconst...
来源: 评论
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation
Cross-Domain Adaptive Clustering for Semi-Supervised Domain ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Li, Jichang Li, Guanbin Shi, Yemin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Deepwise AI Lab Beijing Peoples R China
In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly di... 详细信息
来源: 评论
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Conv...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Yang, Xu Deng, Cheng Dang, Zhiyuan Wei, Kun Yan, Junchi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China
Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two funda... 详细信息
来源: 评论
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration
S<SUP>2</SUP>-BNN: Bridging the Gap Between Self-Supervised ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Shen, Zhiqiang Liu, Zechun Qin, Jie Huang, Lei Cheng, Kwang-Ting Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China Inception Inst Artificial Intelligence Abu Dhabi U Arab Emirates
Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural networks (BNNs), this task has not yet be... 详细信息
来源: 评论
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
Rethinking Class Relations: Absolute-relative Supervised and...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Hongguang Koniusz, Piotr Jian, Songlei Li, Hongdong Torr, Philip H. S. AMS Syst Engn Inst Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia Univ Oxford Oxford England Natl Univ Def Technol Changsha Hunan Peoples R China
The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of... 详细信息
来源: 评论
Deep Dual Consecutive Network for Human Pose Estimation
Deep Dual Consecutive Network for Human Pose Estimation
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liu, Zhenguang Chen, Haoming Feng, Runyang Wu, Shuang Ji, Shouling Yang, Bailin Wang, Xun Zhejiang Gongshang Univ Hangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Zhejiang Univ Hangzhou Zhejiang Peoples R China
Multi-frame human pose estimation in complicated situations is challenging. Although state-of-the-art human joints detectors have demonstrated remarkable results for static images, their performances come short when w... 详细信息
来源: 评论
Quasi-Dense Similarity Learning for Multiple Object Tracking
Quasi-Dense Similarity Learning for Multiple Object Tracking
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Pang, Jiangmiao Qiu, Linlu Li, Xia Chen, Haofeng Li, Qi Darrell, Trevor Yu, Fisher Zhejiang Univ Hangzhou Zhejiang Peoples R China Georgia Inst Technol Atlanta GA 30332 USA Swiss Fed Inst Technol Zurich Switzerland Stanford Univ Stanford CA 94305 USA Univ Calif Berkeley Berkeley CA USA
Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the maj... 详细信息
来源: 评论