计算机科学
人工智能
机器学习
代表(政治)
集合(抽象数据类型)
动作(物理)
深度学习
编码(集合论)
动作识别
透视图(图形)
物理
程序设计语言
法学
政治
量子力学
班级(哲学)
政治学
作者
Wentao Bao,Qi Yu,Yu Kong
标识
DOI:10.1109/iccv48922.2021.01310
摘要
In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamics and static bias of human actions. In this paper, we propose a Deep Evidential Action Recognition (DEAR) method to recognize actions in an open testing set. Specifically, we formulate the action recognition problem from the evidential deep learning (EDL) perspective and propose a novel model calibration method to regularize the EDL training. Besides, to mitigate the static bias of video representation, we propose a plug-and-play module to debias the learned representation through contrastive learning. Experimental results show that our DEAR method achieves consistent performance gain on multiple mainstream action recognition models and benchmarks. Code and pre-trained models are available at https://www.rit.edu/actionlab/dear.
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