lambert-x/video-semisup
Learning from Temporal Gradient for Semi-supervised Action Recognition (CVPR 2022)
This project helps computer vision researchers and AI developers improve action recognition systems by learning from temporal gradients. It takes video datasets (like UCF101 or Kinetics400) and outputs a more robust model for identifying actions in videos, even with limited labeled data. Researchers working on video analytics or surveillance applications would find this useful.
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Use this if you are developing AI models for recognizing human actions in video and need to improve performance, especially when you have a lot of unlabeled video data.
Not ideal if you are not a computer vision researcher or developer, or if you are looking for a ready-to-use application rather than a research implementation.
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30
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2
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 01, 2022
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