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arXiv · 2308.09897

Spatial-Temporal Alignment Network for Action Recognition

Jinhui Ye·Junwei Liang
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Abstract

This paper studies introducing viewpoint invariant feature representations in existing action recognition architecture. Despite significant progress in action recognition, efficiently handling geometric variations in large-scale datasets remains challenging. To tackle this problem, we propose a novel Spatial-Temporal Alignment Network (STAN), which explicitly learns geometric invariant representations for action recognition. Notably, the STAN model is light-weighted and generic, which could be plugged into existing action recognition models (e.g., MViTv2) with a low extra computational cost. We test our STAN model on widely-used datasets like UCF101 and HMDB51. The experimental results show that the STAN model can consistently improve the state-of-the-art models in action recognition tasks in trained-from-scratch settings.

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Jinhui Ye, Junwei Liang. 2023-08-19. Spatial-Temporal Alignment Network for Action Recognition. https://arxiv.org/abs/2308.09897

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