Intra- and Inter-Action Understanding via Temporal Action Parsing

CVPR, pp. 727-736, 2020.

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In Temporal Action Parsing of Olympics Sports, we provide each instance with a class label, and a high-quality temporal parsing annotation at the granularity of sub-actions, which is found to be beneficial for sophisticated action understanding

Abstract:

Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their internal structures...More

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