RenHuan1999/CVPR2023_P-MIL
The official implementation of 'Proposal-based Multiple Instance Learning for Weakly-supervised Temporal Action Localization' (CVPR 2023)
This project helps video analysts and researchers automatically identify and precisely locate specific actions within long video clips, even when only general descriptions of the video content are available. It takes video features and existing action proposals as input, then outputs detailed temporal boundaries for each action. This is ideal for professionals working with large volumes of video footage for content analysis or surveillance.
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Use this if you need to pinpoint the exact start and end times of actions in videos, but only have access to video-level labels for training.
Not ideal if you already have precisely timestamped action labels for every action in your training videos, as this method is designed for weaker supervision.
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Language
Python
License
MIT
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Last pushed
Jun 01, 2023
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