Pilhyeon/Learning-Action-Completeness-from-Points
Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)
This project helps video analysis researchers precisely pinpoint the start and end times of specific actions within long video recordings. It takes in video features extracted from raw video files and a sparse set of labeled frames for each action instance. The output is a highly accurate temporal localization of actions, even rivaling methods that require more extensive, frame-by-frame annotation.
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Use this if you need to identify the exact duration of actions in video with minimal manual labeling effort.
Not ideal if you lack the technical expertise to set up and run a deep learning model, or if your primary interest is in high-level video classification rather than precise temporal action localization.
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Language
Python
License
MIT
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Last pushed
Sep 05, 2023
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