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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops"
23198 条 记 录,以下是4881-4890 订阅
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CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval  29
CP-mtML: Coupled Projection multi-task Metric Learning for L...
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2016 ieee conference on computer vision and pattern recognition (CVPR)
作者: Bhattarai, Binod Sharma, Gaurav Jurie, Frederic Univ Caen Caen France MPI Informat Saarbrucken Germany IIT Kanpur Kanpur Uttar Pradesh India
We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the pr... 详细信息
来源: 评论
Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension  30
Are You Smarter Than A Sixth Grader? Textbook Question Answe...
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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kembhavi, Aniruddha Seo, Minjoon Schwenk, Dustin Choi, Jonghyun Farhadi, Ali Hajishirzi, Hannaneh Allen Inst Artificial Intelligence Seattle WA 98013 USA Univ Washington Seattle WA 98195 USA
We introduce the task of Multi-Modal Machine Comprehension ((MC)-C-3), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) datase... 详细信息
来源: 评论
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations
Fingerprinting Deep Neural Networks Globally via Universal A...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Peng, Zirui Li, Shaofeng Chen, Guoxing Zhang, Cheng Zhu, Haojin Xue, Minhui Shanghai Jiao Tong Univ Shanghai Peoples R China Ohio State Univ Columbus OH 43210 USA CSIRO Data61 Canberra ACT Australia Univ Adelaide Adelaide SA Australia
In this paper, we propose a novel and practical mechanism to enable the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the prof... 详细信息
来源: 评论
Neumann Network with Recursive Kernels for Single Image Defocus Deblurring
Neumann Network with Recursive Kernels for Single Image Defo...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Quan, Yuhui Wu, Zicong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore
Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant s... 详细信息
来源: 评论
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds  32
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for S...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Gu, Xiuye Wang, Yijie Wu, Chongruo Lee, Yong Jae Wang, Panqu Stanford Univ Stanford CA 94305 USA TuSimple San Diego CA USA Univ Calif Davis Davis CA 95616 USA
We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBC... 详细信息
来源: 评论
GROUNDHOG : Grounding Large Language Models to Holistic Segmentation
GROUNDHOG : Grounding Large Language Models to Holistic Segm...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Yichi Qiao, Zhiqiao Gao, Xiaofeng Shakiah, Suhaila Gao, Qiaozi Chai, Joyce Univ Michigan Ann Arbor MI 48109 USA Amazon AGI Seattle WA USA
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm la... 详细信息
来源: 评论
LARGE-SCALE STEREO vision  11
LARGE-SCALE STEREO VISION
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conference A: computer vision and Applications, at the 11th IAPR International conference on pattern recognition
作者: TAKAHASHI, H TOMITA, F SANYO ELECT CO TSUKUBA RES CTRTSUKUBAIBARAKI 305JAPAN
In the case of stereo measuring in the 3-dimensional world, it is difficult to obtain a sufficient accuracy in an outside environment. We propose to move a single camera and to prolong the base line, then measure the ... 详细信息
来源: 评论
Patch-based Progressive 3D Point Set Upsampling  32
Patch-based Progressive 3D Point Set Upsampling
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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang Yifan Shihao Wu Hui Huang Cohen-Or, Daniel Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland Shenzhen Univ Shenzhen Peoples R China Tel Aviv Univ Tel Aviv Israel
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image su... 详细信息
来源: 评论
Pixel-Adaptive Convolutional Neural Networks  32
Pixel-Adaptive Convolutional Neural Networks
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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Su, Hang Jampani, Varun Sun, Deqing Gallo, Orazio Learned-Miller, Erik Kautz, Jan UMass Amherst Amherst MA 01003 USA NVIDIA Santa Clara CA USA
Convolutions are the fundamental building blocks of CNNs. The fact that their weights are spatially shared is one of the main reasons for their widespread use, but it is also a major limitation, as it makes convolutio... 详细信息
来源: 评论
Efficient Fine-grained Classification and Part Localization Using One Compact Network  16
Efficient Fine-grained Classification and Part Localization ...
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16th ieee International conference on computer vision (ICCV)
作者: Dai, Xiyang Southall, Ben Nhon Trinh Matei, Bogdan Univ Maryland College Pk MD 20742 USA SRI Int Princeton NJ USA
Fine-grained classification of objects such as vehicles, natural objects and other classes is an important problem in visual recognition. It is a challenging task because small and localized differences between simila... 详细信息
来源: 评论