Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Weakly Semantic Guided Action Recognition | |
Yu, Tingzhao1,2 ; Wang, Lingfeng1; Da, Cheng1,2; Gu, Huxiang1; Xiang, Shiming1; Pan, Chunhong1
| |
| Source Publication | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| ISSN | 1520-9210 |
| 2019-10-01 | |
| Volume | 21Issue:10Pages:2504-2517 |
| Corresponding Author | Yu, Tingzhao(tingzhao.yu@nlpr.ia.ac.cn) |
| Abstract | Action recognition plays a fundamental role in computer vision and video analysis. Nevertheless, extracting effective spatial-temporal features remains a challenging task. This paper proposes three simple but effective weakly semantic guided modules (SGMs) for both environment-constrained and cross-domain action recognition. The SGMs are composed of total 3-D convolution and element-wise gated operations; thus, they are efficient and easy to implement. The semantic guidance is obtained in a weakly supervised manner, in which each video clip is labeled with only an action class instead of pixel-level semantics. Benefitting from the semantic guidance, the network [called semantic guided network (SGN)] can focus on the salient parts of the video clips. Consequently, the redundant information can be reduced and the model is more robust to noise. Besides, benefitting from the intrinsic property of SGMs, SGN is totally end-to-end trainable. Quantities of experiments on both environment-constrained (e.g., Penn, HMDB-51, and UCF-101) and cross-domain (e.g., ODAR) action recognition datasets demonstrate its effectiveness. Specifically, SGN gets improvements of 3.7%, 2.1%, and 5.2% for Penn, HMDB-51, and UCF-101 than the baseline ResNet3D, respectively, and SGN ranked third place in the ODAR 2017 challenge. |
| Keyword | Semantic guided module action recognition cross domain 3D convolution attention model |
| DOI | 10.1109/TMM.2019.2907060 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[91438105] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[91438105] |
| Funding Organization | National Natural Science Foundation of China |
| WOS Research Area | Computer Science ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| WOS ID | WOS:000489728400007 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/23708 |
| Collection | 模式识别国家重点实验室_先进时空数据分析与学习 |
| Corresponding Author | Yu, Tingzhao |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Yu, Tingzhao,Wang, Lingfeng,Da, Cheng,et al. Weakly Semantic Guided Action Recognition[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(10):2504-2517. |
| APA | Yu, Tingzhao,Wang, Lingfeng,Da, Cheng,Gu, Huxiang,Xiang, Shiming,&Pan, Chunhong.(2019).Weakly Semantic Guided Action Recognition.IEEE TRANSACTIONS ON MULTIMEDIA,21(10),2504-2517. |
| MLA | Yu, Tingzhao,et al."Weakly Semantic Guided Action Recognition".IEEE TRANSACTIONS ON MULTIMEDIA 21.10(2019):2504-2517. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| [Tsingzao]Weakly Sem（18774KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment