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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops"
12859 条 记 录,以下是4421-4430 订阅
Language-guided Multi-modal Emotional Mimicry Intensity Estimation
Language-guided Multi-modal Emotional Mimicry Intensity Esti...
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ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)
作者: Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland
Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expre... 详细信息
来源: 评论
FACESEC: A Fine-grained Robustness Evaluation Framework for Face recognition Systems
FACESEC: A Fine-grained Robustness Evaluation Framework for ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Tong, Liang Chen, Zhengzhang Ni, Jingchao Cheng, Wei Song, Dongjin Chen, Haifeng Vorobeychik, Yevgeniy Washington Univ St Louis MO 14263 USA NEC Labs Amer Princeton NJ 08540 USA Univ Connecticut Storrs CT USA
We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixe... 详细信息
来源: 评论
The Blessings of Unlabeled Background in Untrimmed Videos
The Blessings of Unlabeled Background in Untrimmed Videos
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liu, Yuan Chen, Jingyuan Chen, Zhenfang Deng, Bing Huang, Jianqiang Zhang, Hanwang Alibaba Grp Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore
Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from th... 详细信息
来源: 评论
PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model
PointPrompt: A Multi-modal Prompting Dataset for Segment Any...
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ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)
作者: Jorge Quesada Mohammad Alotaibi Mohit Prabhushankar Ghassan AlRegib OLIVES Lab Georgia Institute of Technology Atlanta GA USA
The capabilities of foundation models, most recently the Segment Anything Model, have gathered a large degree of attention for providing a versatile framework for tackling a wide array of image segmentation tasks. How... 详细信息
来源: 评论
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Changpinyo, Soravit Sharma, Piyush Ding, Nan Soricut, Radu Google Res Mountain View CA 94043 USA
The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collecte... 详细信息
来源: 评论
LQF: Linear Quadratic Fine-Tuning
LQF: Linear Quadratic Fine-Tuning
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Achille, Alessandro Golatkar, Aditya Ravichandran, Avinash Polito, Marzia Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA Univ Calif Los Angeles Los Angeles CA 90024 USA
Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such des... 详细信息
来源: 评论
LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results
LFNAT 2023 Challenge on Light Field Depth Estimation: Method...
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ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)
作者: Hao Sheng Yebin Liu Jingyi Yu Gaochang Wu Wei Xiong Ruixuan Cong Rongshan Chen Longzhao Guo Yanlin Xie Shuo Zhang Song Chang Youfang Lin Wentao Chao Xuechun Wang Guanghui Wang Fuqing Duan Tun Wang Da Yang Zhenglong Cui Sizhe Wang Mingyuan Zhao Qiong Wang Qianyu Chen Zhengyu Liang Yingqian Wang Jungang Yang Xueting Yang Junli Deng LFNAT 2023 Challenge State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University and Beihang Hangzhou Innovation Institute Yuhang
This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adja...
来源: 评论
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence
PatchMatch-Based Neighborhood Consensus for Semantic Corresp...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Lee, Jae Yong DeGol, Joseph Fragoso, Victor Sinha, Sudipta N. Univ Illinois Chicago IL 60680 USA Microsoft Washington DC USA
We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the... 详细信息
来源: 评论
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
Fashion IQ: A New Dataset Towards Retrieving Images by Natur...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wu, Hui Gao, Yupeng Guo, Xiaoxiao Al-Halah, Ziad Rennie, Steven Grauman, Kristen Feris, Rogerio MIT IBM Watson AI Lab Cambridge MA 02142 USA IBM Res Armonk NY 10504 USA UT Austin Austin TX USA Pryon New York NY USA
Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ datase... 详细信息
来源: 评论
Variational Prototype Learning for Deep Face recognition
Variational Prototype Learning for Deep Face Recognition
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Deng, Jiankang Guo, Jia Yang, Jing Lattas, Alexandros Zafeiriou, Stefanos Huawei Shenzhen Peoples R China Imperial Coll London England InsightFace London England Univ Nottingham Nottingham England
Deep face recognition has achieved remarkable improvements due to the introduction of margin-based soft-max loss, in which the prototype stored in the last linear layer represents the center of each class. In these me... 详细信息
来源: 评论