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Local context attention learning for fine-grained scene graph generation 期刊论文
PATTERN RECOGNITION, 2024, 卷号: 156, 页码: 13
作者:  Zhu, Xuhan;  Wang, Ruiping;  Lan, Xiangyuan;  Wang, Yaowei
收藏  |  浏览/下载：2/0  |  提交时间：2024/12/06
Fine-grained scene graph generation  Local context  Location attention network  Local context-consistent label transfer  
Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors 期刊论文
SENSORS, 2024, 卷号: 24, 期号: 19, 页码: 21
作者:  Tang, Shuyuan;  Zhou, Yiqing;  Li, Jintao;  Liu, Chang;  Shi, Jinglin
收藏  |  浏览/下载：1/0  |  提交时间：2024/12/06
pedestrian detection  computer vision  attention-guided feature enhancement  convolutional neural network  
Infant cry classification using an efficient graph structure and attention-based model 期刊论文
KUWAIT JOURNAL OF SCIENCE, 2024, 卷号: 51, 期号: 3, 页码: 9
作者:  Qiao, Xuesong;  Jiao, Siwen;  Li, Han;  Liu, Gengyuan;  Gao, Xuan;  Li, Zhanshan
收藏  |  浏览/下载：1/0  |  提交时间：2024/12/06
Neural network  Multi-head attention  Infant cry  Audio classification  
STWave+: A Multi-Scale Efficient Spectral Graph Attention Network With Long-Term Trends for Disentangled Traffic Flow Forecasting 期刊论文
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 卷号: 36, 期号: 6, 页码: 2671-2685
作者:  Fang, Yuchen;  Qin, Yanjun;  Luo, Haiyong;  Zhao, Fang;  Zheng, Kai
收藏  |  浏览/下载：1/0  |  提交时间：2024/12/06
Market research  Forecasting  Roads  Time series analysis  Sensors  Correlation  Encoding  Contrastive learning  graph attention network  spatio-temporal data  traffic forecasting  
Multi-feature deep supervised voiceprint adversarial network for depression recognition from speech 期刊论文
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 卷号: 89, 页码: 15
作者:  Pan, Yuchen;  Shang, Yuanyuan;  Wang, Wei;  Shao, Zhuhong;  Han, Zhuojin;  Liu, Tie;  Guo, Guodong;  Ding, Hui
收藏  |  浏览/下载：19/0  |  提交时间：2024/05/20
Adversarial learning  Audio processing  Attention mechanism  Deep neural network  Depression recognition  Feature enhancement  
Fpar: filter pruning via attention and rank enhancement for deep convolutional neural networks acceleration 期刊论文
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2024, 页码: 13
作者:  Chen, Yanming;  Wu, Gang;  Shuai, Mingrui;  Lou, Shubin;  Zhang, Yiwen;  An, Zhulin
收藏  |  浏览/下载：17/0  |  提交时间：2024/05/20
Neural network  Model compression  Filter pruning  Attention  Rank enhancement  CNNs  
SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2024, 卷号: 19, 页码: 6604-6619
作者:  Liu, Zizhen;  He, Weiyang;  Chang, Chip-Hong;  Ye, Jing;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：2/0  |  提交时间：2024/12/06
Data models  Servers  Training  Hidden Markov models  Training data  Adaptation models  Security  Federated learning  poisoning attack  knowledge distillation  attention maps  deep neural network  
Semantic and Correlation Disentangled Graph Convolutions for Multilabel Image Recognition 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13
作者:  Cai, Shaofei;  Li, Liang;  Han, Xinzhe;  Huang, Shan;  Tian, Qi;  Huang, Qingming
收藏  |  浏览/下载：14/0  |  提交时间：2024/05/20
Attention mechanism  feature disentangling  graph convolutional network (GCN)  multilabel recognition  
Contour attention network for cerebrovascular segmentation from TOF-MRA volumetric images 期刊论文
MEDICAL PHYSICS, 2023, 页码: 12
作者:  Yang, Chaozhi;  Zhang, Haiyan;  Chi, Dianwei;  Li, Yachuan;  Xiao, Qian;  Bai, Yun;  Li, Zongmin;  Li, Hongyi;  Li, Hua
收藏  |  浏览/下载：20/0  |  提交时间：2023/12/04
3D U-Net  cerebrovascular segmentation  contour attention network  one-voxel-thick contour generation  
Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation 期刊论文
INFORMATION SCIENCES, 2023, 卷号: 624, 页码: 324-343
作者:  Zhu, Xiaofei;  Tang, Gu;  Wang, Pengfei;  Li, Chenliang;  Guo, Jiafeng;  Dietze, Stefan
收藏  |  浏览/下载：29/0  |  提交时间：2023/07/12
Recommendation system  Session-based recommendation  Graph neural network  Behavior modeling  Attention model  Representation learning