MULL'21: First International Workshop on Multimedia Understanding with Less Labeling

被引:0
|
作者
Wei, Xiu-Shen [1 ]
Yang, Jufeng [2 ]
Ye, Han-Jia [3 ]
Yang, Jian [4 ]
机构
[1] Nanjing Univ Sci & Technol, Key Lab Intelligent Percept & Syst High Dimens In, Minist Educ, Nanjing, Peoples R China
[2] Nankai Univ, Dept Comp Sci & Technol, Tianjin, Peoples R China
[3] Nanjing Univ, Sch Artificial Intelligence, Nanjing, Peoples R China
[4] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing, Peoples R China
关键词
less labeling; multimedia understanding; few-shot learning; self-supervised learning; weakly-supervised learning;
D O I
10.1145/3474085.3478581
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the advent of deep neural networks, quite a lot of multimedia tasks have been significantly improved. While, however, deep neural networks still lack the ability of learning from less labeling, e.g., with limited exemplars or fast generalizing to new tasks. In order to address the current inefficiency of multimedia, there is pressing need to research methods to drastically reduce requirements for labeled training data. This workshop aims to provide a platform for discussing the challenges and corresponding innovative approaches in multimedia with less labeling. We hope more advanced technologies can be proposed or inspired, and also we invite several domain-specific experts for sharing their insights and research progress on the topic of MULL.
引用
收藏
页码:5704 / 5707
页数:4
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