Trajectory-aligned Space-time Tokens for Few-shot Action Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917733486034944 |
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| author | Kumar, Pulkit Padmanabhan, Namitha Luo, Luke Rambhatla, Sai Saketh Shrivastava, Abhinav |
| author_facet | Kumar, Pulkit Padmanabhan, Namitha Luo, Luke Rambhatla, Sai Saketh Shrivastava, Abhinav |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18249 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trajectory-aligned Space-time Tokens for Few-shot Action Recognition Kumar, Pulkit Padmanabhan, Namitha Luo, Luke Rambhatla, Sai Saketh Shrivastava, Abhinav Computer Vision and Pattern 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 |
| title | Trajectory-aligned Space-time Tokens for Few-shot Action Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.18249 |