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检索条件"机构=Key Laboratory of Services Computing Technology and System"
1800 条 记 录,以下是181-190 订阅
排序:
Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing
Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing
In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbala... 详细信息
来源: 评论
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space
arXiv
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arXiv 2025年
作者: Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China
Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. H... 详细信息
来源: 评论
Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification
Multi-View Representation Learning for Multi-Instance Learni...
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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
作者: Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China
Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the... 详细信息
来源: 评论
Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification
Deep Multi-Instance Learning with Adaptive Recurrent Pooling...
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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
作者: Ding, Yi Zhao, Lu Yuan, Liming Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China
Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key i... 详细信息
来源: 评论
Object Segmentation-Assisted Inter Prediction for Versatile Video Coding
arXiv
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arXiv 2024年
作者: Li, Zhuoyuan Yuan, Zikun Li, Li Liu, Dong Tang, Xiaohu Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China
In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, re... 详细信息
来源: 评论
Service Migration in Mec: An Approach Based on Deep Reinforcement Learning and Fuzzy Logic
SSRN
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SSRN 2023年
作者: Zhang, Xinyang Bu, Chao Wang, Jinsong Key Laboratory of Computer Vision System of Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China
As a new computing paradigm, Mobile Edge computing (MEC) sinks resources to the network edge to support real-time re-sponses of services. Facing the demand for migrating services caused by the frequent movement of the... 详细信息
来源: 评论
Learning Topic Emotion and Logical Semantic for Video Paragraph Captioning
SSRN
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SSRN 2024年
作者: Li, Qinyu Wang, Hanli Yi, Xiaokai Department of Computer Science & Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai200092 China
Video paragraph captioning aims to generate multiple descriptive sentences for videos, which strive to replicate human writing in accuracy, logicality, and richness. However, current research focuses on the accuracy a... 详细信息
来源: 评论
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning
arXiv
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arXiv 2024年
作者: Du, Xiaohu Wen, Ming Zhu, Jiahao Xie, Zifan Ji, Bin Liu, Huijun Shi, Xuanhua Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China College of Computer National University of Defense Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab HUST Wuhan430074 China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security HUST Wuhan430074 China JinYinHu Laboratory Wuhan430077 China Cluster and Grid Computing Lab HUST Wuhan430074 China
Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source ... 详细信息
来源: 评论
A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media  28th
A Topic-Aware Graph-Based Neural Network for User Interest ...
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28th International Conference on Database systems for Advanced Applications, DASFAA 2023
作者: Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China
User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) fr... 详细信息
来源: 评论
Generative artificial intelligence and its applications in materials science:Current situation and future perspectives
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Journal of Materiomics 2023年 第4期9卷 798-816页
作者: Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia
Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learni... 详细信息
来源: 评论