Summary of Action-agnostic Point-level Supervision For Temporal Action Detection, by Shuhei M. Yoshida et al.
Action-Agnostic Point-Level Supervision for Temporal Action Detection
by Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed action-agnostic point-level (AAPL) supervision method aims to achieve accurate action instance detection using a lightly annotated dataset. By sampling frames from videos without human intervention, AAPL reduces the annotation cost while maintaining competitive or outperforming detection performance compared to prior methods. The approach is evaluated on various datasets, including THUMOS ’14, FineAction, GTEA, BEOID, and ActivityNet 1.3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AAPL supervision makes it easier to detect actions in videos by having humans label only a small part of the video frames without needing to search for every action instance. The method also includes a detection model and learning approach to effectively use these labels. By using AAPL, less data is needed to train models that can recognize actions. |
