Trajectory-Aligned Space-Time Tokens for Few-Shot Action Recognition
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 here.
Cite
Text
Kumar et al. "Trajectory-Aligned Space-Time Tokens for Few-Shot Action Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-72764-1_27Markdown
[Kumar et al. "Trajectory-Aligned Space-Time Tokens for Few-Shot Action Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/kumar2024eccv-trajectoryaligned/) doi:10.1007/978-3-031-72764-1_27BibTeX
@inproceedings{kumar2024eccv-trajectoryaligned,
title = {{Trajectory-Aligned Space-Time Tokens for Few-Shot Action Recognition}},
author = {Kumar, Pulkit and Padmanabhan, Namitha and Luo, Luke and Rambhatla, Sai Saketh and Shrivastava, Abhinav},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-72764-1_27},
url = {https://mlanthology.org/eccv/2024/kumar2024eccv-trajectoryaligned/}
}