  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
{\small{\url{https://www.rit.edu/actionlab/dear}}}.
