arXiv:1212.0402
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
원문 초록
We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%. To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
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MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos
EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Joint Temporal Pooling for Improving Skeleton-based Action Recognition
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