What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

TL;DR

This paper introduces a multitask learning framework for action quality assessment that leverages related tasks like recognition and commentary generation, demonstrating improved performance and generalization on a new large dataset.

Contribution

It proposes a novel multitask learning approach for AQA, integrating multiple related tasks, and introduces the largest dataset to date for evaluating action quality assessment methods.

Findings

MTL approach outperforms STL across architectures

C3D-AVG-MTL achieves 90.44% rank correlation

MTL provides better generalization than STL

Abstract

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features that explain three related tasks - fine-grained action recognition, commentary generation, and estimating the AQA score. A new multitask-AQA dataset, the largest to date, comprising of 1412 diving samples was collected to evaluate our approach (https://github.com/ParitoshParmar/MTL-AQA). We show that our MTL approach outperforms STL approach using two different kinds of architectures: C3D-AVG and MSCADC. The C3D-AVG-MTL approach achieves the new state-of-the-art performance with a rank correlation of 90.44%. Detailed experiments were performed to show that MTL offers better generalization than STL, and representations from action recognition models are not sufficient for the AQA task and instead should be learned.