Evidential Deep Learning for Open Set Action Recognition
This addresses the challenge of handling out-of-distribution actions in real-world video scenarios, which is incremental as it builds on existing evidential deep learning and contrastive learning techniques.
The paper tackles the problem of open-set action recognition, where models must recognize known actions and reject unknown ones in videos, by proposing a Deep Evidential Action Recognition (DEAR) method that includes model calibration and a debiasing module, achieving consistent performance gains on multiple benchmarks.
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}}}.