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What is Multimodal Learning for Abnormal Action Recognition about?

This research article presents a novel approach for video abnormal action recognition using multimodal heterogeneous transfer learning, which leverages text descriptions associated with videos to improve model performance despite limited labeled data. The proposed framework introduces a Sequential Human Action Normalized Descriptor (SHAND) that simplifies and accelerates the representation of human actions, enhancing the training of recognition models. The study also introduces a new dataset, the Abnormal A

Author

23204021017

Language

EN