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An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
(3) We achieve the same performance with only 10% of the original data for training as with all of the original data from the real-world dataset, and a better performance on In-the-wild videos, by employing our data augmentation techniques.
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition
Current datasets for action recognition tasks face limitations stemming from traditional collection and generation methods, including the constrained range of action classes, absence of multi-viewpoint recordings, limited diversity, poor video quality, and labor-intensive manually collection.
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