Temporal Action Localization with Multi-temporal Scales

Zan Gao; Xinglei Cui; Tao Zhuo; Zhiyong Cheng; An-An Liu; Meng Wang; Shenyong Chen

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arXiv · 2208.07493

Temporal Action Localization with Multi-temporal Scales

Zan Gao·Xinglei Cui·Tao Zhuo·Zhiyong Cheng·An-An Liu·Meng Wang·Shenyong Chen

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Abstract

Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. The previous methods often predict actions on a feature space of a single-temporal scale. However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries. To address this issue, we propose to predict actions on a feature space of multi-temporal scales. Specifically, we use refined feature pyramids of different scales to pass semantics from high-level scales to low-level scales. Besides, to establish the long temporal scale of the entire video, we use a spatial-temporal transformer encoder to capture the long-range dependencies of video frames. Then the refined features with long-range dependencies are fed into a classifier for the coarse action prediction. Finally, to further improve the prediction accuracy, we propose to use a frame-level self attention module to refine the classification and boundaries of each action instance. Extensive experiments show that the proposed method can outperform state-of-the-art approaches on the THUMOS14 dataset and achieves comparable performance on the ActivityNet1.3 dataset. Compared with A2Net (TIP20, Avg\{0.3:0.7\}), Sub-Action (CSVT2022, Avg\{0.1:0.5\}), and AFSD (CVPR21, Avg\{0.3:0.7\}) on the THUMOS14 dataset, the proposed method can achieve improvements of 12.6\%, 17.4\% and 2.2\%, respectively

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BibTeX RIS

Zan Gao, Xinglei Cui, Tao Zhuo, Zhiyong Cheng, An-An Liu, Meng Wang, Shenyong Chen. 2022-08-16. Temporal Action Localization with Multi-temporal Scales. https://arxiv.org/abs/2208.07493

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