Temporal Action Unit Perception Based Open Set Action Recognition
YANG Kaixiang1, GAO Junyu2, FENG Yangbo1, XU Changsheng2
1. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300382; 2. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract：In open set action recognition tasks, a model is requested to identify categories within the training set accurately and reject unknown actions that never appear in the training set. Currently, most of the methods treat the action as a whole, ignoring the fact that the action can be decomposed into finer-grained action units. To address this issue, a method for temporal action unit perception based open set action recognition is proposed in this paper. Firstly, an action unit relationship module is designed to learn fine-grained features of action units, and thus the relational pattern between actions and action units is obtained. The unknown actions are identified according to the different degrees of activation of known and unknown actions on action units. Secondly, an action unit temporal module is designed to model the temporal information of action units. The temporal characteristics of action units are explored to further distinguish between known actions and unknown actions that are visually similar but confusable with each other. Finally, with comprehensive consideration of both relational patterns and temporal information of action units, the model is equipped with the capability of distinguishing known actions from unknown actions. Experimental results on three action recognition datasets demonstrate the superior performance of the proposed method.
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2023, Vol. 36 
