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检索条件"机构=Center of Computer Vision and Robotics Research Computer Science Department"
804 条 记 录,以下是11-20 订阅
DUAL STUDENT NETWORKS FOR DATA-FREE MODEL STEALING  11
DUAL STUDENT NETWORKS FOR DATA-FREE MODEL STEALING
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11th International Conference on Learning Representations, ICLR 2023
作者: Beetham, James Kardan, Navid Mian, Ajmal Shah, Mubarak Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States Department of Computer Science University of Western Australia CrawleyWA6009 Australia
Data-free model stealing aims to replicate a target model without direct access to either the training data or the target model. To accomplish this, existing methods use a generator to produce samples in order to trai... 详细信息
来源: 评论
Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images
Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...
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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
作者: Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China
Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heter... 详细信息
来源: 评论
Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition
Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-S...
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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
作者: Wu, Wenhan Hua, Yilei Zheng, Ce Wu, Shiqian Chen, Chen Lu, Aidong University of North Carolina at Charlotte Department of Computer Science United States School of Information Science and Engineering Wuhan University of Science and Technology China Center for Research in Computer Vision University of Central Florida United States
Self-supervised skeleton-based action recognition has attracted more attention in recent years. By utilizing the unlabeled data, more generalizable features can be learned to alleviate the overfitting problem and redu... 详细信息
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Exploiting autoencoder’s weakness to generate pseudo anomalies
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Neural Computing and Applications 2024年 第23期36卷 14075-14091页
作者: Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training da... 详细信息
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Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer  24
Frequency Guidance Matters: Skeletal Action Recognition by F...
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32nd ACM International Conference on Multimedia, MM 2024
作者: Wu, Wenhan Zheng, Ce Yang, Zihao Chen, Chen Das, Srijan Lu, Aidong Department of Computer Science University of North Carolina at Charlotte CharlotteNC United States Robotics Institute Carnegie Mellon University PittsburghPA United States One Inventory Organization Microsoft Corporation RedmondWA United States Center for Research in Computer Vision University of Central Florida OrlandoFL United States
Recently, transformers have demonstrated great potential for modeling long-term dependencies from skeleton sequences and thereby gained ever-increasing attention in skeleton action recognition. However, the existing t... 详细信息
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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation
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International Journal of computer vision 2025年 1-20页
作者: Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China
While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target ... 详细信息
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BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics
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Proceedings of the ACM on Human-computer Interaction 2024年 第CSCW1期8卷 1-31页
作者: Liu, Shiyi Ma, Ruofei Zhao, Chuyi Li, Zhenbang Xiao, Jianpeng Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science Tufts University MedfordMA United States Guangzhou Qucheng Culture Media Co. Guangzhou China School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China
Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximat... 详细信息
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LEARNING SEMANTIC PROXIES FROM VISUAL PROMPTS FOR PARAMETER-EFFICIENT FINE-TUNING IN DEEP METRIC LEARNING
arXiv
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arXiv 2024年
作者: Ren, Li Chen, Chen Wang, Liqiang Hua, Kien Department of Computer Science University of Central Florida United States Center for Research in Computer Vision
Deep Metric Learning (DML) has long attracted the attention of the machine learning community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained models on conventional image datasets. As... 详细信息
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Numerical Method for Complete Solution of the Optimal Control Problem  9
Numerical Method for Complete Solution of the Optimal Contro...
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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023
作者: Diveev, Askhat Federal Research Center 'Computer Science and Control' The Russian Academy of Sciences Department of Robotics Control Vavilova str. 44 Moscow119333 Russia
The work is devoted to the numerical complete solution of the optimal control problem. The complete solution means the solution of the optimal control problem together with the solution of the control synthesis proble... 详细信息
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Machine Learning Control Synthesis by Symbolic Regression for Avoidance of Arbitrary Positioned Obstacles  9
Machine Learning Control Synthesis by Symbolic Regression fo...
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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023
作者: Shmalko, Elizaveta Diveev, Askhat Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences The Department of Robotics Control Vavilova str. 44 Moscow119333 Russia
The problem of control synthesis for a mobile robot with phase constraints in the form of an arbitrarily located obstacle is formulated. To solve the problem, a numerical method of machine learning based on symbolic r... 详细信息
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