Temporal Action Proposal Generation with Transformers

• URL: http://arxiv.org/abs/2105.12043v1
• Date: Tue, 25 May 2021 16:22:12 GMT
• Title: Temporal Action Proposal Generation with Transformers
• Authors: Lining Wang, Haosen Yang, Wenhao Wu, Hongxun Yao, Hujie Huang
• Abstract summary: This paper intuitively presents a unified temporal action proposal generation framework with original Transformers. The Boundary Transformer captures long-term temporal dependencies to predict precise boundary information. The Proposal Transformer learns the rich inter-proposal relationships for reliable confidence evaluation.
• Score: 25.66256889923748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer networks are effective at modeling long-range contextual information and have recently demonstrated exemplary performance in the natural language processing domain. Conventionally, the temporal action proposal generation (TAPG) task is divided into two main sub-tasks: boundary prediction and proposal confidence prediction, which rely on the frame-level dependencies and proposal-level relationships separately. To capture the dependencies at different levels of granularity, this paper intuitively presents a unified temporal action proposal generation framework with original Transformers, called TAPG Transformer, which consists of a Boundary Transformer and a Proposal Transformer. Specifically, the Boundary Transformer captures long-term temporal dependencies to predict precise boundary information and the Proposal Transformer learns the rich inter-proposal relationships for reliable confidence evaluation. Extensive experiments are conducted on two popular benchmarks: ActivityNet-1.3 and THUMOS14, and the results demonstrate that TAPG Transformer outperforms state-of-the-art methods. Equipped with the existing action classifier, our method achieves remarkable performance on the temporal action localization task. Codes and models will be available.

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