Institute of Computing Technology, Chinese Academy IR
| Multi-Domain and Multi-Task Learning for Human Action Recognition | |
| Liu, An-An1; Xu, Ning1; Nie, Wei-Zhi1; Su, Yu-Ting1; Zhang, Yong-Dong2,3 | |
| 2019-02-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| ISSN | 1057-7149 |
| 卷号 | 28期号:2页码:853-867 |
| 摘要 | Domain-invariant (view-invariant and modality-invariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain and multi-task learning (MDMTL) method to: 1) extract domain-invariant information for multi-view and multi-modal action representation and 2) explore the relatedness among multiple action categories. Specifically, we present a sparse transfer learning-based method to co-embed multi-domain (multi-view and multi-modality) data into a single common space for discriminative feature learning. Additionally, visual feature learning is incorporated into the multi-task learning framework, with the Frobenius-norm regularization term and the sparse constraint term, for joint task modeling and task relatedness-induced feature learning. To the best of our knowledge, MDMTL is the first supervised framework to jointly realize domain-invariant feature learning and task modeling for multi-domain action recognition. Experiments conducted on the INRIA Xmas Motion Acquisition Sequences data set, the MSR Daily Activity 3D (DailyActivity3D) data set, and the Multi-modal & Multi-view & Interactive data set, which is the most recent and largest multi-view and multi-model action recognition data set, demonstrate the superiority of MDMTL over the state-of-the-art approaches. |
| 关键词 | Domain-invariant Learning multi-task learning human action recognition |
| DOI | 10.1109/TIP.2018.2872879 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Natural Science Foundation of China[61772359] ; National Natural Science Foundation of China[61472275] ; National Natural Science Foundation of China[61525206] ; National Natural Science Foundation of China[61872267] ; National Natural Science Foundation of China[61502337] ; National Key Research and Development Program of China[2017YFC0820600] ; National Defense Science and Technology Fund for Distinguished Young Scholars[2017-JCJQ-ZQ-022] |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000448501800002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/3651 |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, An-An; Xu, Ning; Nie, Wei-Zhi |
| 作者单位 | 1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Anhui, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, An-An,Xu, Ning,Nie, Wei-Zhi,et al. Multi-Domain and Multi-Task Learning for Human Action Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(2):853-867. |
| APA | Liu, An-An,Xu, Ning,Nie, Wei-Zhi,Su, Yu-Ting,&Zhang, Yong-Dong.(2019).Multi-Domain and Multi-Task Learning for Human Action Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(2),853-867. |
| MLA | Liu, An-An,et al."Multi-Domain and Multi-Task Learning for Human Action Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.2(2019):853-867. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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