Papers › What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
Paritosh Parmar, Brendan Tran Morris
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Quality Assessment | MTL-AQA | C3D-AVG-MTL | Spearman Correlation | 90.44 | #17 of 21 | Archive leaderboard | report |
| Action Quality Assessment | MTL-AQA | C3D-AVG-STL | Spearman Correlation | 89.60 | #18 of 21 | Archive leaderboard | report |
| Action Quality Assessment | MTL-AQA | MSCADC-MTL | Spearman Correlation | 86.12 | #20 of 21 | Archive leaderboard | report |
| Action Quality Assessment | MTL-AQA | MSCADC-STL | Spearman Correlation | 84.72 | #21 of 21 | Archive leaderboard | report |
| Action Recognition | MTL-AQA | C3D-AVG | Armstand Accuracy | 99.72 % | #1 of 1 | Archive leaderboard | report |
| Action Recognition | MTL-AQA | C3D-AVG | No. of Somersaults Accuracy | 96.88 % | #1 of 1 | Archive leaderboard | report |
| Action Recognition | MTL-AQA | C3D-AVG | No. of Twists Accuracy | 93.20 % | #1 of 1 | Archive leaderboard | report |
| Action Recognition | MTL-AQA | C3D-AVG | Position Accuracy | 96.32 % | #1 of 1 | Archive leaderboard | report |
| Action Recognition | MTL-AQA | C3D-AVG | Rotation Type Accuracy | 97.45 % | #1 of 1 | Archive leaderboard | report |
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