Résumé: Action assessment, the task of visually assessing the quality of performing an action, has attracted much attention in recent years, with promising applications in areas such as medical treatment and sporting events. However, most existing methods of action assessment mainly target the actions performed by a single person; in particular, they neglect the asymmetric relations among agents (e.g., between persons and objects), limiting their performance in many nonindividual actions. In this work, we formulate a framework for modelling asymmetric interactions among agents for action assessment, considering the subordinations among agents in many interactive actions. Specifically, we propose an asymmetric interaction learner consisting of an automatic assigner and an asymmetric interaction network search module. The automatic assigner is designed to automatically group agents within an action into a primary agent (e.g., human) and secondary agents (e.g., objects); the asymmetric interaction network search module adaptively learns the asymmetric interactions between these agents. We conduct experiments on the JIGSAWS dataset containing surgical actions and additionally collect two new datasets, TASD-2 and PaSk, for action assessment on interactive sporting actions. The experimental results on these three datasets demonstrate the effectiveness of our framework in achieving state-of-the-art performance. The extensive experiments on the AQA-7 dataset also indicate the robustness of our model in conventional action assessment settings.

Langue: Anglais