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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"
22908 条 记 录,以下是4961-4970 订阅
Coronal Mass Ejection Tracking Through Curve-Fitting
Coronal Mass Ejection Tracking Through Curve-Fitting
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SoutheastCon conference
作者: Suzuki, Jeren Newman, Timothy S. Univ Alabama Dept Comp Sci Huntsville AL 35899 USA
A scheme for tracking solar coronal mass ejections (CMEs) is presented. The scheme operates on images from the Solar TErrestrial RElations Observatory (STEREO) satellite. The focus of the scheme is tracking the leadin... 详细信息
来源: 评论
Discriminative pattern Calibration Mechanism for Source-Free Domain Adaptation
Discriminative Pattern Calibration Mechanism for Source-Free...
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conference on computer vision and pattern recognition (CVPR)
作者: Haifeng Xia Siyu Xia Zhengming Ding School of Automation Southeast University Department of Computer Science Tulane University
Source-free domain adaptation (SFDA) assumes that model adaptation only accesses the well-learned source model and unlabeled target instances for knowledge trans-fer. However, cross-domain distribution shift easily tr... 详细信息
来源: 评论
Autoregressive Stylized Motion Synthesis with Generative Flow
Autoregressive Stylized Motion Synthesis with Generative Flo...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wen, Yu-Hui Yang, Zhipeng Fu, Hongbo Gao, Lin Sun, Yanan Liu, Yong-Jin Tsinghua Univ BNRist CS Dept Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Sch Creat Media Hong Kong Peoples R China Chinese Acad Sci Beijing Key Lab Mobile Comp & Pervas Device ICT Beijing Peoples R China
Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics. Most existing deep learning methods for this problem are superv... 详细信息
来源: 评论
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Anomaly Detection in Video via Self-Supervised and Multi-Tas...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Georgescu, Mariana-Iuliana Barbalau, Antonio Ionescu, Radu Tudor Khan, Fahad Shahbaz Popescu, Marius Shah, Mubarak Univ Bucharest Bucharest Romania MBZ Univ Artificial Intelligence Abu Dhabi U Arab Emirates SecurifAI Bucharest Romania Univ Cent Florida Orlando FL 32816 USA
Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this pa... 详细信息
来源: 评论
Goal-Oriented Gaze Estimation for Zero-Shot Learning
Goal-Oriented Gaze Estimation for Zero-Shot Learning
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Yang Zhou, Lei Bai, Xiao Huang, Yifei Gu, Lin Zhou, Jun Harada, Tatsuya Beihang Univ Sch Comp Sci & Engn Jiangxi Res Inst State Key Lab Software Dev Environm Beijing Peoples R China Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan Griffith Univ Brisbane Qld Australia
Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Since semantic knowledge is built on attributes shared between different classes, which ... 详细信息
来源: 评论
Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance
Impact of Pseudo Depth on Open World Object Segmentation wit...
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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)
作者: Robin Schön Katja Ludwig Rainer Lienhart Chair for Machine Learning and Computer Vision University of Augsburg
Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that have never been seen during training...
来源: 评论
Learning Placeholders for Open-Set recognition
Learning Placeholders for Open-Set Recognition
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhou, Da-Wei Ye, Han-Jia Zhan, De-Chuan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China
Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input of unknown categories, and the model ... 详细信息
来源: 评论
Dynamic Class Queue for Large Scale Face recognition In the Wild
Dynamic Class Queue for Large Scale Face Recognition In the ...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Bi Xi, Teng Zhang, Gang Feng, Haocheng Han, Junyu Liu, Jingtuo Ding, Errui Liu, Wenyu Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China
Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on comput... 详细信息
来源: 评论
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
STaR: Self-supervised Tracking and Reconstruction of Rigid O...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yuan, Wentao Lv, Zhaoyang Schmidt, Tanner Lovegrove, Steven Univ Washington Seattle WA 98195 USA Facebook Real Labs Res Menlo Pk CA USA
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural... 详细信息
来源: 评论
Learning to See in Nighttime Driving Scenes with Inter-frequency Priors
Learning to See in Nighttime Driving Scenes with Inter-frequ...
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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)
作者: Zhentao Fan Xianhao Wu Xiang Chen Yufeng Li College of Electronic and Information Engineering Shenyang Aerospace University School of Computer Science and Engineering Nanjing University of Science and Technology
Currently, image-to-image translation methods have achieved significant performance with the help of deep CNNs and GANs, but most existing models are not suitable for autonomous driving scenarios due to their interpre...
来源: 评论