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检索条件"机构=State Key Laboratory Software Engineering and School Computer and Complex Network Research Center"
540 条 记 录,以下是121-130 订阅
排序:
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
Cross-Modal Implicit Relation Reasoning and Aligning for Tex...
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Conference on computer Vision and Pattern Recognition (CVPR)
作者: Ding Jiang Mang Ye National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China
Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. ...
来源: 评论
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
arXiv
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arXiv 2023年
作者: Jiang, Ding Ye, Mang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China
Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. ... 详细信息
来源: 评论
Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics
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research 2024年 第1期6卷 707-720页
作者: Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China
Data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data... 详细信息
来源: 评论
Look from Coarse to Fine: A Hierarchical Lane Detection network
Look from Coarse to Fine: A Hierarchical Lane Detection Netw...
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Artificial Intelligence Innovation (ICAII), International Conference on
作者: Xichen Yu Yanduo Zhang Tao Lu Xun Li Jun Chang Hubei Provincial Key Laboratory of Intelligent Robot School of Computer Science and Engineering & School of Artificial Intelligence Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and School of Computer Science Wuhan University Wuhan China
Lane detection involves identifying a high-level semantic identifier with a slender structure, which requires both high-level feature detection for existence and low-level features for shape and position determination...
来源: 评论
Fine-Grained Noisy Segment Learning for Fatigue Detection
SSRN
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SSRN 2024年
作者: Wang, Mei Hu, Ruimin Liao, Liang Wang, Xiaochen Chen, Xiaohe Hu, Jinzhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China The Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore The Wuhan Research Institute of Posts and Telecommunications Wuhan430072 China
In fatigue detection, fine-grained labels (seconds-based) commonly inherit coarse-grained labels (minutes-based or more). However, due to the dynamic and time-varying nature of fatigue states, ``Noisy Segments'... 详细信息
来源: 评论
Token Contrast for Weakly-Supervised Semantic Segmentation
Token Contrast for Weakly-Supervised Semantic Segmentation
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Conference on computer Vision and Pattern Recognition (CVPR)
作者: Lixiang Ru Heliang Zheng Yibing Zhan Bo Du Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China JD Explore Academy China
Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure perception of CNN, CAM usually cannot...
来源: 评论
Hyperspectral Image-Text Coupling network For Hyperspectral Image Classification
Hyperspectral Image-Text Coupling Network For Hyperspectral ...
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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)
作者: Jiaqi Yang Bo Du State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan P. R. China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan P. R. China
Deep learning-based approaches have blossomed in the field of hyperspectral image (HSI) classification. However, most of the existing methods focus only on visual information and ignore textual clues. Textual properti... 详细信息
来源: 评论
Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring
Generating Targeted Universal Adversarial Perturbation again...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Yujun Zhang Yanqu Chen Jiakai Wang Jin Hu Renshuai Tao Xianglong Liu State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China
There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constra... 详细信息
来源: 评论
Imperceptible and Reliable Adversarial Attack  1
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4th International Conference on Frontiers in Cyber Security, FCS 2021
作者: Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States
Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them ... 详细信息
来源: 评论
Contributing Dimension Structure of Deep Feature for Coreset Selection
arXiv
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arXiv 2024年
作者: Wan, Zhijing Wang, Zhixiang Wang, Yuran Wang, Zheng Zhu, Hongyuan Satoh, Shinichi National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering China The University of Tokyo Japan National Institute of Informatics Japan A*STAR Singapore
Coreset selection seeks to choose a subset of crucial training samples for efficient learning. It has gained traction in deep learning, particularly with the surge in training dataset sizes. Sample selection hinges on... 详细信息
来源: 评论