Trajectory-aligned Space-time Tokens for Few-shot Action Recognition
<|reference_start|>Trajectory-aligned Space-time Tokens for Few-shot Action Recognition: 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<|reference_end|>
@article{kumar2024trajectory-aligned,
    title={Trajectory-aligned Space-time Tokens for Few-shot Action Recognition},
    author={Pulkit Kumar, Namitha Padmanabhan, Luke Luo, Sai Saketh Rambhatla,
  Abhinav Shrivastava},
    journal={arXiv preprint arXiv:2407.18249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18249},
    primaryClass={cs.CV}
}