Open-Vocabulary Action Localization with Iterative Visual Prompting

Video action localization aims to find the timings of specific actions from a long video. Although existing learning-based approaches have been successful, they require annotating videos, which comes with a considerable labor cost. This paper proposes a learning-free, open-vocabulary approach based on emerging off-the-shelf vision-language models (VLMs). The challenge stems from the fact that VLMs are neither designed to process long videos nor tailored for finding actions. We overcome these problems by extending an iterative visual prompting technique. Specifically, we sample video frames and create a concatenated image with frame index labels, making a VLM guess a frame that is considered to be closest to the start and end of the action. Iterating this process by narrowing a sampling time window results in finding the specific frames corresponding to the start and end of an action. We demonstrate that this technique yields reasonable performance, achieving results comparable to state-of-the-art zero-shot action localization. These results illustrate a practical extension of VLMs for understanding videos. A sample code is available at https://microsoft.github.io/VLM-Video-Action-Localization/.