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arXiv · 2407.18249

Trajectory-aligned Space-time Tokens for Few-shot Action Recognition

Pulkit Kumar·Namitha Padmanabhan·Luke Luo·Sai Saketh Rambhatla·Abhinav Shrivastava
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Abstract

We propose a simple yet effective approach for few-shot action recognition, emphasizing the disentanglement of motion and appearance representations. By harnessing recent progress in tracking, specifically point trajectories and self-supervised representation learning, we build trajectory-aligned tokens (TATs) that capture motion and appearance information. This approach significantly reduces the data requirements while retaining essential information. To process these representations, we use a Masked Space-time Transformer that effectively learns to aggregate information to facilitate few-shot action recognition. We demonstrate state-of-the-art results on few-shot action recognition across multiple datasets. Our project page is available at https://www.cs.umd.edu/~pulkit/tats

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Pulkit Kumar, Namitha Padmanabhan, Luke Luo, Sai Saketh Rambhatla, Abhinav Shrivastava. 2024-07-25. Trajectory-aligned Space-time Tokens for Few-shot Action Recognition. https://arxiv.org/abs/2407.18249

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