Action-Agnostic Point-Level Supervision for Temporal Action Detection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909444675207168 |
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| author | Yoshida, Shuhei M. Shibata, Takashi Terao, Makoto Okatani, Takayuki Sugiyama, Masashi |
| author_facet | Yoshida, Shuhei M. Shibata, Takashi Terao, Makoto Okatani, Takayuki Sugiyama, Masashi |
| contents | We propose action-agnostic point-level (AAPL) supervision for temporal action detection to achieve accurate action instance detection with a lightly annotated dataset. In the proposed scheme, a small portion of video frames is sampled in an unsupervised manner and presented to human annotators, who then label the frames with action categories. Unlike point-level supervision, which requires annotators to search for every action instance in an untrimmed video, frames to annotate are selected without human intervention in AAPL supervision. We also propose a detection model and learning method to effectively utilize the AAPL labels. Extensive experiments on the variety of datasets (THUMOS '14, FineAction, GTEA, BEOID, and ActivityNet 1.3) demonstrate that the proposed approach is competitive with or outperforms prior methods for video-level and point-level supervision in terms of the trade-off between the annotation cost and detection performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Action-Agnostic Point-Level Supervision for Temporal Action Detection Yoshida, Shuhei M. Shibata, Takashi Terao, Makoto Okatani, Takayuki Sugiyama, Masashi Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We propose action-agnostic point-level (AAPL) supervision for temporal action detection to achieve accurate action instance detection with a lightly annotated dataset. In the proposed scheme, a small portion of video frames is sampled in an unsupervised manner and presented to human annotators, who then label the frames with action categories. Unlike point-level supervision, which requires annotators to search for every action instance in an untrimmed video, frames to annotate are selected without human intervention in AAPL supervision. We also propose a detection model and learning method to effectively utilize the AAPL labels. Extensive experiments on the variety of datasets (THUMOS '14, FineAction, GTEA, BEOID, and ActivityNet 1.3) demonstrate that the proposed approach is competitive with or outperforms prior methods for video-level and point-level supervision in terms of the trade-off between the annotation cost and detection performance. |
| title | Action-Agnostic Point-Level Supervision for Temporal Action Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.21205 |