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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"
21010 条 记 录,以下是1681-1690 订阅
排序:
Detector-Free Weakly Supervised Group Activity recognition
Detector-Free Weakly Supervised Group Activity Recognition
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Kim, Dongkeun Lee, Jinsung Cho, Minsu Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea
Group activity recognition is the task of understanding the activity conducted by a group of people as a whole in a multi-person video. Existing models for this task are often impractical in that they demand ground-tr... 详细信息
来源: 评论
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
Exact Feature Distribution Matching for Arbitrary Style Tran...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Zhang, Yabin Li, Minghan Li, Ruihuang Jia, Kui Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China South China Univ Technol Guangzhou Peoples R China
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the assumption of Gaussian feature ... 详细信息
来源: 评论
Learned Queries for Efficient Local Attention
Learned Queries for Efficient Local Attention
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Arar, Moab Shamir, Ariel Bermano, Amit H. Tel Aviv Univ Tel Aviv Israel Reichman Univ Herzliyya Israel
vision Transformers (ViT) serve as powerful vision models. Unlike convolutional neural networks, which dominated vision research in previous years, vision transformers enjoy the ability to capture long-range dependenc... 详细信息
来源: 评论
Leveraging Self-Supervision for Cross-Domain Crowd Counting
Leveraging Self-Supervision for Cross-Domain Crowd Counting
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Liu, Weizhe Durasov, Nikita Fua, Pascal Tencent AI Lab Bellevue WA 98004 USA Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good per... 详细信息
来源: 评论
3D Scene Painting via Semantic Image Synthesis
3D Scene Painting via Semantic Image Synthesis
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Jeong, Jaebong Jo, Janghun Cho, Sunghyun Park, Jaesik POSTECH GSAI & CSE Pohang South Korea
We propose a novel approach to 3D scene painting using a configurable 3D scene layout. Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color va... 详细信息
来源: 评论
Iterative Deep Homography Estimation
Iterative Deep Homography Estimation
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Cao, Si-Yuan Hu, Jianxin Sheng, Zehua Shen, Hui-Liang Zhejiang Univ Hangzhou Peoples R China
We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator;th... 详细信息
来源: 评论
Salvage of Supervision in Weakly Supervised Object Detection
Salvage of Supervision in Weakly Supervised Object Detection
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Sui, Lin Zhang, Chen-Lin Wu, Jianxin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China 4Paradigm Inc Beijing Peoples R China
Weakly supervised object detection (WSOD) has recently attracted much attention. However, the lack of bounding-box supervision makes its accuracy much lower than fully supervised object detection (FSOD), and currently... 详细信息
来源: 评论
Dense Depth Priors for Neural Radiance Fields from Sparse Input Views
Dense Depth Priors for Neural Radiance Fields from Sparse In...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Roessle, Barbara Barron, Jonathan T. Mildenhall, Ben Srinivasan, Pratul P. Niebner, Matthias Tech Univ Munich Munich Germany Google Res Mountain View CA USA
Neural radiance fields (NeRF) encode a scene into a neural representation that enables photo-realistic rendering of novel views. However, a successful reconstruction from RGB images requires a large number of input vi... 详细信息
来源: 评论
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
TransforMatcher: Match-to-Match Attention for Semantic Corre...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Kim, Seungwook Min, Juhong Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this work, we introduce a strong semantic ima... 详细信息
来源: 评论
Learning to Align Sequential Actions in the Wild
Learning to Align Sequential Actions in the Wild
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Liu, Weizhe Tekin, Bugra Coskun, Huseyin Vineet, Vibhav Fua, Pascal Pollefeys, Marc Tencent AI Lab Bellevue WA 98004 USA Microsoft Redmond WA USA Tech Univ Munich Munich Germany Ecole Polytech Fed Lausanne Lausanne Switzerland Swiss Fed Inst Technol Zurich Switzerland
State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame mapping across sequences, which does not... 详细信息
来源: 评论