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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 3: AAAI-22 Technical Tracks 3

Temporal Action Proposal Generation with Background Constraint

February 1, 2023

Authors

Haosen Yang

Harbin Institute of Technology Department of Computer Vision Technology (VIS), Baidu Inc


Wenhao Wu

Department of Computer Vision Technology (VIS), Baidu Inc


Lining Wang

Harbin Institute of Technology


Sheng Jin

Harbin Institute of Technology


Boyang Xia

Institute of Computing Technology, Chinese Academy of Science Department of Computer Vision Technology (VIS), Baidu Inc


Hongxun Yao

Harbin Institute of Technology


Hujie Huang

Harbin Institute of Technology


Proceedings:

No. 3: AAAI-22 Technical Tracks 3

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision III

Downloads:

Download PDF

Abstract:

Temporal action proposal generation (TAPG) is a challenging task that aims to locate action instances in untrimmed videos with temporal boundaries. To evaluate the confidence of proposals, the existing works typically predict action score of proposals that are supervised by the temporal Intersection-over-Union (tIoU) between proposal and the ground-truth. In this paper, we innovatively propose a general auxiliary Background Constraint idea to further suppress low-quality proposals, by utilizing the background prediction score to restrict the confidence of proposals. In this way, the Background Constraint concept can be easily plug-and-played into existing TAPG methods (BMN, GTAD). From this perspective, we propose the Background Constraint Network (BCNet) to further take advantage of the rich information of action and background. Specifically, we introduce an Action-Background Interaction module for reliable confidence evaluation, which models the inconsistency between action and background by attention mechanisms at the frame and clip levels. Extensive experiments are conducted on two popular benchmarks, ActivityNet-1.3 and THUMOS14. The results demonstrate that our method outperforms state-of-the-art methods. Equipped with the existing action classifier, our method also achieves remarkable performance on the temporal action localization task.

DOI:

10.1609/aaai.v36i3.20212


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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