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检索条件"机构=Key Laboratory of Computer Vision and Machine Learning"
334 条 记 录,以下是1-10 订阅
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Fusion of data dimensionality reduction algorithms baced on category representation theory  4
Fusion of data dimensionality reduction algorithms baced on ...
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4th International Conference on computer vision, Application, and Algorithm, CVAA 2024
作者: Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China
For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. Therefore, it is a long-term and exploratory work to study machine learning representatio... 详细信息
来源: 评论
Discriminative Score Suppression for Weakly Supervised Video Anomaly Detection
Discriminative Score Suppression for Weakly Supervised Video...
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2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025
作者: Xu, Chen Li, Chunguo Xing, Hongjie College of Mathematics and Information Science Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding071002 China School of Cyber Security and Computer Hebei University Baoding071000 China
Weakly supervised video anomaly detection (WSVAD) often relies on Multiple Instance learning (MIL). However, selecting only the most discriminative segments for training limits the model's ability to comprehensive... 详细信息
来源: 评论
Missing Customized Distillation Network for Incomplete Multimodal Sentiment Analysis  27th
Missing Customized Distillation Network for Incomplete Multi...
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27th International Conference on Pattern Recognition, ICPR 2024
作者: Hu, Zhangfeng Zheng, Wenming Wei, Mengting Shi, Mengxin Zong, Yuan Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing210096 China School of Biological Science and Medical Engineering Southeast University Nanjing210096 China Pazhou Laboratory Guangzhou510320 China Center for Machine Vision and Signal Analysis Oulu University Oulu90570 Finland
The fusion of multimodal cues, i.e., visual, audio, and language, can provide complementary insights and benefit sentiment analysis. However, not all of them are always available in practical scenarios, posing a chall... 详细信息
来源: 评论
learning to see speckle in the weak laser field through multimode fiber
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Optoelectronics Letters 2025年
作者: JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), the Key Laboratory of Computer Vision and Systems (Ministry of Education), and the School of Computer Science and Engineering, Tianjin University of Technology
Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of s...
来源: 评论
SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive learning for Efficient Cross-Emotion Anomaly Detection
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IEEE Transactions on Cognitive and Developmental Systems 2025年
作者: Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China
Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different ... 详细信息
来源: 评论
DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation
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computers, Materials & Continua 2025年 第3期82卷 4841-4862页
作者: Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China
The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels re... 详细信息
来源: 评论
SCSegamba: Lightweight Structure-Aware vision Mamba for Crack Segmentation in Structures
arXiv
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arXiv 2025年
作者: Liu, Hui Jia, Chen Shi, Fan Cheng, Xu Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and System Ministry of Education
Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in ... 详细信息
来源: 评论
CRLNet: Cascaded Resolution learning Network for Natural Scenes Segmentation
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IEEE Intelligent Systems 2025年
作者: Li, Wei Tian, Shishun Hua, Guoguang Liao, Muxin Zhang, Yuhang Zou, Wenbin Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Electronics and Information Engineering Shenzhen518060 China Jiangxi Agricultural University School of Computer Science and Engineering Nanchang330045 China
The natural environment presents a multitude of scenes with diverse content, posing challenges for satisfactory segmentation results using existing segmentation networks. In response, we propose a Cascaded Resolution ... 详细信息
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Harnessing Light Field Angular Cues and Spatial Geometries for Semantic Segmentation
Harnessing Light Field Angular Cues and Spatial Geometries f...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Chen Jia Fan Shi Xu Cheng School of Computer Science and Engineering The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) The Key Laboratory of Computer Vision and System (Ministry of Education) Tianjin University of Technology Tianjin China
4D light field imaging captures rich spatial-angular information, providing essential geometric cues for semantic segmentation tasks. In this paper, we introduce a novel backbone network called the Light Field Extract... 详细信息
来源: 评论
Discriminative Score Suppression for Weakly Supervised Video Anomaly Detection
Discriminative Score Suppression for Weakly Supervised Video...
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IEEE Workshop on Applications of computer vision (WACV)
作者: Chen Xu Chunguo Li Hongjie Xing Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China School of Cyber Security and Computer Hebei University Baoding China
Weakly supervised video anomaly detection (WSVAD) often relies on Multiple Instance learning (MIL). However, selecting only the most discriminative segments for training limits the model's ability to comprehensive... 详细信息
来源: 评论