Point-supervised temporal action localisation based on multi-branch attention-Reference-Cited by-同舟云学术

Point-supervised temporal action localisation based on multi-branch attention

Published:2023-04-13 Issue:11 Volume:17 Page:
ISSN:1751-7575
Container-title:Enterprise Information Systems
language:en
Short-container-title:Enterprise Information Systems

Author:

Liu Shuai¹²³,Zhang Yang³,Srivastava Gautam⁴⁵⁶^ORCID

Affiliation:

1. School of Educational Science, Hunan Normal University, Changsha, China

2. Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha, China

3. College of Computer Science and Engineering, Hunan Normal University, Changsha, China

4. Department of Math and Computer Science, Brandon University, Brandon, Canada

5. Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan

6. Department of Computer Science and Math, Lebanese American University, Beirut, Lebanon

Publisher

Informa UK Limited

Subject

Information Systems and Management,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/17517575.2023.2197318

Reference39 articles.

1. Bai, Y., Y. Wang, Y. Tong, Y. Yang, Q. Liu, and J. Liu Boundary Content Graph Neural Network for Temporal Action Proposal Generation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, (pp. 1546–1563). Springer International Publishing.

2. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

3. Rethinking the Faster R-CNN Architecture for Temporal Action Localization

4. Movement Pattern Histogram for Action Recognition and Retrieval

5. Fan, M., L. Zhu, Y. Yang, S. Zha, G. Kundu, M. Feiszli, and Z. Shou. 2020. “Sf-Net: Single-Frame Supervision for Temporal Action Localization.” In Computer Vision–ECCV 2020: 16th European conference on computer vision, Glasgow, UK, August 23–28, 2020, Proceedings, Part IV Vol. 16, 420–437. Springer International Publishing.

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