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Adaptive Deep Metric Learning for Affective Image Retrieval and Classification 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 1640-1653
作者:  Yao, Xingxu;  She, Dongyu;  Zhang, Haiwei;  Yang, Jufeng;  Cheng, Ming-Ming;  Wang, Liang
收藏  |  浏览/下载：213/0  |  提交时间：2021/08/15
Measurement  Visualization  Semantics  Feature extraction  Task analysis  Image analysis  Image retrieval  Affective image retrieval  convolutional neural network  deep metric learning  visual sentiment analysis  
Unified Cross-domain Classification via Geometric and Statistical Adaptations 期刊论文
PATTERN RECOGNITION, 2021, 卷号: 110, 页码: 9
作者:  Liu, Weifeng;  Li, Jinfeng;  Liu, Baodi;  Guan, Weili;  Zhou, Yicong;  Xu, Changsheng
收藏  |  浏览/下载：200/0  |  提交时间：2021/01/06
Domain adaptation  Statistical adaptation  Maximum mean discrepancy (MMD)  Geometric adaptation  Nystrom method  
Self-Paced Balance Learning for Clinical Skin Disease Recognition 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 8, 页码: 2832-2846
作者:  Yang, Jufeng;  Wu, Xiaoping;  Liang, Jie;  Sun, Xiaoxiao;  Cheng, Ming-Ming;  Rosin, Paul L.;  Wang, Liang
收藏  |  浏览/下载：227/0  |  提交时间：2020/09/07
Diseases  Skin  Complexity theory  Training  Task analysis  Image recognition  Learning systems  Class imbalance  clinical skin disease recognition  complexity level  self-paced balance learning (SPBL)  
Uncertainty-optimized deep learning model for small-scale person re-identification 期刊论文
SCIENCE CHINA-INFORMATION SCIENCES, 2019, 卷号: 62, 期号: 12, 页码: 13
作者:  Zhao, Cairong;  Chen, Kang;  Zang, Di;  Zhang, Zhaoxiang;  Zuo, Wangmeng;  Mia, Duoqian
收藏  |  浏览/下载：266/0  |  提交时间：2020/03/30
person re-identification  uncertainty analysis  deep learning  
Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 8, 页码: 3973-3985
作者:  Liang, Jie;  Yang, Jufeng;  Cheng, Ming-Ming;  Rosin, Paul L.;  Wang, Liang
收藏  |  浏览/下载：284/0  |  提交时间：2019/09/30
Subspace clustering  triplet relationship  estimating the number of clusters  hyper-graph clustering