Action Quality Assessment Across Multiple Actions
This work addresses the problem of efficiently assessing action quality for applications like sports or healthcare, though it is incremental by building on existing AQA methods.
The paper investigates whether knowledge transfer can improve action quality assessment (AQA) across multiple actions, finding that a single model trained on consolidated samples from seven actions enhances performance, as demonstrated on a new dataset of 1106 samples with expert scores.
Can learning to measure the quality of an action help in measuring the quality of other actions? If so, can consolidated samples from multiple actions help improve the performance of current approaches? In this paper, we carry out experiments to see if knowledge transfer is possible in the action quality assessment (AQA) setting. Experiments are carried out on our newly released AQA dataset (http://rtis.oit.unlv.edu/datasets.html) consisting of 1106 action samples from seven actions with quality scores as measured by expert human judges. Our experimental results show that there is utility in learning a single model across multiple actions.