Summary of Open-vocabulary Action Localization with Iterative Visual Prompting, by Naoki Wake et al.
Open-Vocabulary Action Localization with Iterative Visual Prompting
by Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu, Katsushi Ikeuchi
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 This research proposes a novel, learning-free approach to video action localization using off-the-shelf vision-language models (VLMs). Traditional methods require annotating videos, which is labor-intensive. The proposed method overcomes the limitations of VLMs by extending an iterative visual prompting technique, sampling video frames with frame index labels and narrowing the time window iteratively. This yields reasonable performance, comparable to state-of-the-art zero-shot action localization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer models called vision-language models (VLMs) to find specific actions in long videos without needing to label each part of the video. Current methods require a lot of work to do this labeling, so this new approach is helpful. The scientists used a way to ask the VLMs questions about which parts of the video are most likely to be the start and end of an action. By repeating this process, they were able to find the exact frames that show the actions starting and ending. |
Keywords
» Artificial intelligence » Prompting » Zero shot
