yassersouri/MuCon
Official Implementation for "Fast Weakly Supervised Action Segmentation Using Mutual Consistency" - TPAMI 2021
This tool helps researchers and practitioners in video analysis automatically break down long, unlabelled videos of human actions into distinct, meaningful segments. You provide a video dataset (like the Breakfast dataset with I3D features), and it outputs metrics on how well the video was segmented into individual actions, even with minimal human input. It's designed for someone working on understanding complex human activities from video footage.
No commits in the last 6 months.
Use this if you need to quickly and efficiently segment human actions in video datasets without extensive manual annotation.
Not ideal if your primary goal is real-time video processing or if you require fine-grained, frame-level action recognition rather than broader segmentation.
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Language
Python
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Last pushed
Aug 30, 2021
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