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Collaborative Local-Global Learning for Temporal Action Proposal

文献类型：期刊论文

作者Zhu, Yisheng2; Han, Hu1; Liu, Guangcan2; Liu, Qingshan2
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2021-12-01
卷号12期号:5页码:14
关键词Temporal action proposal generation untrimmed videos long short-term memory attention mechanism
ISSN号2157-6904
DOI10.1145/3466181
英文摘要Temporal action proposal generation is an essential and challenging task in video understanding, which aims to locate the temporal intervals that likely contain the actions of interest. Although great progress has been made, the problem is still far from being well solved. In particular, prevalent methods can handle well only the local dependencies (i.e., short-term dependencies) among adjacent frames but are generally powerless in dealing with the global dependencies (i.e., long-term dependencies) between distant frames. To tackle this issue, we propose CLGNet, a novel Collaborative Local-Global Learning Network for temporal action proposal. The majority of CLGNet is an integration of Temporal Convolution Network and Bidirectional Long Short-Term Memory, in which Temporal Convolution Network is responsible for local dependencies while Bidirectional Long Short-Term Memory takes charge of handling the global dependencies. Furthermore, an attention mechanism called the background suppression module is designed to guide our model to focus more on the actions. Extensive experiments on two benchmark datasets, THUMOS'14 and ActivityNet-1.3, show that the proposed method can outperform state-of-the-art methods, demonstrating the strong capability of modeling the actions with varying temporal durations.
资助项目New Generation AI Major Project of Ministry of Science and Technology of China[2018AAA0102501]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000732997200004
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/18005]  
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, Guangcan
作者单位1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Nanjing Univ Infor Mat Sci & Technol, Sch Automat, 219 NingLiu Rd, Nanjing 210000, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Yisheng,Han, Hu,Liu, Guangcan,et al. Collaborative Local-Global Learning for Temporal Action Proposal[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2021,12(5):14.
APA Zhu, Yisheng,Han, Hu,Liu, Guangcan,&Liu, Qingshan.(2021).Collaborative Local-Global Learning for Temporal Action Proposal.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,12(5),14.
MLA Zhu, Yisheng,et al."Collaborative Local-Global Learning for Temporal Action Proposal".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 12.5(2021):14.

入库方式： OAI收割

来源：计算技术研究所

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