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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"
22906 条 记 录,以下是4721-4730 订阅
排序:
Recurrent Scene Parsing with Perspective Understanding in the Loop  31
Recurrent Scene Parsing with Perspective Understanding in th...
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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kong, Shu Fowlkes, Charless Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA
Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aware gating module that adaptively sel... 详细信息
来源: 评论
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
Can Neural Nets Learn the Same Model Twice? Investigating Re...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Somepalli, Gowthami Fowl, Liam Bansal, Arpit Yeh-Chiang, Ping Dar, Yehuda Baraniuk, Richard Goldblum, Micah Goldstein, Tom Univ Maryland College Pk MD 20742 USA Rice Univ Houston TX 77251 USA NYU New York NY 10003 USA
We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visualizations to investigate issues related to reproducibility and generalization in neural network training. W... 详细信息
来源: 评论
Rethinking Visual Geo-localization for Large-Scale Applications
Rethinking Visual Geo-localization for Large-Scale Applicati...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Berton, Gabriele Masone, Carlo Caputo, Barbara Politecn Torino Turin Italy CINI Turin Italy
Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perfor... 详细信息
来源: 评论
Learning to Predict Activity Progress by Self-Supervised Video Alignment
Learning to Predict Activity Progress by Self-Supervised Vid...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Donahue, Gerard Elhamifar, Ehsan Northwestern Univ Boston MA 02115 USA
In this paper, we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses differ... 详细信息
来源: 评论
Semantic Component Decomposition for Face Attribute Manipulation  32
Semantic Component Decomposition for Face Attribute Manipula...
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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chen, Ying-Cong Shen, Xiaohui Lin, Zhe Lu, Xin Pao, I-Ming Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Youtu Lab Shanghai Peoples R China Adobe Res San Jose CA USA ByteDance AI Lab Beijing Peoples R China
Deep neural network-based methods were proposed for face attribute manipulation. There still exist, however, two major issues, i.e., insufficient visual quality (or resolution) of the results and lack of user control.... 详细信息
来源: 评论
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
GIRAFFE: Representing Scenes as Compositional Generative Neu...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Niemeyer, Michael Geiger, Andreas Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany
Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigat... 详细信息
来源: 评论
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
MASA-SR: Matching Acceleration and Spatial Adaptation for Re...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lu, Liying Li, Wenbo Tao, Xin Lu, Jiangbo Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Kuaishou Beijing Peoples R China SmartMore Hong Kong Peoples R China
Reference-based image super-resolution (RefSR) has shown promising success in recovering high frequency details by utilizing an external reference image (Ref). In this task, texture details are transferred from the Re... 详细信息
来源: 评论
A Conservative Approach for Unbiased Learning on Unknown Biases
A Conservative Approach for Unbiased Learning on Unknown Bia...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Jeon, Myeongho Kim, Daekyung Lee, Woochul Kang, Myungjoo Lee, Joonseok Seoul Natl Univ Seoul South Korea Monitor Corp Seoul South Korea
Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data. Existing unb... 详细信息
来源: 评论
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images  32
GIF2Video: Color Dequantization and Temporal Interpolation o...
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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Yang Huang, Haibin Wang, Chuan He, Tong Wang, Jue Minh Hoai SUNY Stony Brook Stony Brook NY USA Megvii Res USA Hangzhou Peoples R China UCLA Los Angeles CA USA
Graphics Interchange Format (GIF) is a highly portable graphics format that is ubiquitous on the Internet. Despite their small sizes, GIF images often contain undesirable visual artifacts such as flat color regions, f... 详细信息
来源: 评论
Matching Feature Sets for Few-Shot Image Classification
Matching Feature Sets for Few-Shot Image Classification
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Afrasiyabi, Arman Larochelle, Hugo Lalonde, Jean-Francois Gagne, Christian Univ Laval Quebec City PQ Canada Google Brain Mountain View CA USA Canada CIFAR AI Chair Toronto ON Canada Mila Montreal PQ Canada
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from thi... 详细信息
来源: 评论