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Progressive Context-Aware Graph Feature Learning for Target Re-Identification 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2023, 卷号: 25, 页码: 1230-1242
作者:  Cao, Min;  Ding, Cong;  Chen, Chen;  Dou, Hao;  Hu, Xiyuan;  Yan, Junchi
收藏  |  浏览/下载：110/0  |  提交时间：2023/11/16
Target re-identification  graph convolutional network  feature learning  contextual information  graph feature learning  
Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2023, 卷号: 25, 页码: 6258-6271
作者:  Xu, Jingzhao;  Yuan, Mengke;  Yan, Dong-Ming;  Wu, Tieru
收藏  |  浏览/下载：18/0  |  提交时间：2024/02/22
Lighting  Wavelet transforms  Image enhancement  Frequency modulation  Wavelet coefficients  Noise reduction  Discrete wavelet transforms  Attention mechanism  illumination guidance  low-light image enhancement  wavelet transform  
Holographic Feature Learning of Egocentric-Exocentric Videos for Multi-Domain Action Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 2273-2286
作者:  Huang, Yi;  Yang, Xiaoshan;  Gao, Junyun;  Xu, Changsheng
Adobe PDF(2409Kb)  |  收藏  |  浏览/下载：305/61  |  提交时间：2022/07/25
Videos  Feature extraction  Visualization  Task analysis  Computational modeling  Target recognition  Prototypes  Egocentric videos  exocentric videos  holographic feature  multi-domain  action recognition  
Human Parsing With Part-Aware Relation Modeling 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 25, 页码: 2601-2612
作者:  Zhang, Xiaomei;  Chen, Yingying;  Tang, Ming;  Wang, Jinqiao;  Zhu, Xiangyu;  Lei, Zhen
Adobe PDF(6053Kb)  |  收藏  |  浏览/下载：101/5  |  提交时间：2023/11/17
Human parsing  modeling  part-aware relation  
The Model May Fit You: User-Generalized Cross-Modal Retrieval 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 24, 页码: 2998-3012
作者:  Ma, Xinhong;  Yang, Xiaoshan;  Gao, Junyu;  Xu, Changsheng
Adobe PDF(6549Kb)  |  收藏  |  浏览/下载：238/46  |  提交时间：2022/06/17
cross-modal retrieval  domain generalization  meta-learning  
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：197/0  |  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  
Visual Question Answering With Dense Inter- and Intra-Modality Interactions 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3518-3529
作者:  Liu, Fei;  Liu, Jing;  Fang, Zhiwei;  Hong, Richang;  Lu, Hanqing
Adobe PDF(2891Kb)  |  收藏  |  浏览/下载：261/58  |  提交时间：2021/12/28
Visualization  Knowledge discovery  Connectors  Encoding  Task analysis  Image coding  Stacking  Visual question answering  attention  dense interactions  
Unsupervised Video Summarization via Relation-Aware Assignment Learning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3203-3214
作者:  Gao, Junyu;  Yang, Xiaoshan;  Zhang, Yingying;  Xu, Changsheng
Adobe PDF(3649Kb)  |  收藏  |  浏览/下载：289/59  |  提交时间：2021/11/03
Feature extraction  Training  Optimization  Semantics  Recurrent neural networks  Task analysis  Graph neural network  unsupervised learning  video summarization  
Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 2386-2397
作者:  Wang, Wei;  Gao, Junyu;  Yang, Xiaoshan;  Xu, Changsheng
Adobe PDF(2165Kb)  |  收藏  |  浏览/下载：306/42  |  提交时间：2021/11/02
Feature extraction  Encoding  Task analysis  Semantics  Data models  Cognition  Focusing  Video-text retrieval  graph neural network  coarse-to-fine strategy  
Single-Image Specular Highlight Removal via Real-World Dataset Construction 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 页码: 12
作者:  Wu ZQ(吴仲琦);  Zhuang CQ(庄传青);  Shi J(石剑);  Guo JW(郭建伟);  Xiao J(肖俊);  Zhang XP(张晓鹏);  Yan DM(严冬明)
Adobe PDF(49307Kb)  |  收藏  |  浏览/下载：241/94  |  提交时间：2022/06/15
Specular highlight removal, PSD-Dataset, Deep learning