Papers › Auto-Encoding Score Distribution Regression for Action Quality Assessment
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Boyu Zhang, Jiayuan Chen, Yinfei Xu, HUI ZHANG, Xu Yang, Xin Geng
The action quality assessment (AQA) of videos is a challenging vision task since the relation between videos and action scores is difficult to model. Thus, AQA has been widely studied in the literature. Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scores. But previous methods ignored data uncertainty in AQA dataset. To address aleatoric uncertainty, we further develop a plug-and-play module Distribution Auto-Encoder (DAE). Specifically, it encodes videos into distributions and uses the reparameterization trick in variational auto-encoders (VAE) to sample scores, which establishes a more accurate mapping between videos and scores. Meanwhile, a likelihood loss is used to learn the uncertainty parameters. We plug our DAE approach into MUSDL and CoRe. Experimental results on public datasets demonstrate that our method achieves state-of-the-art on AQA-7, MTL-AQA, and JIGSAWS datasets. Our code is available at https://github.com/InfoX-SEU/DAE-AQA.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Quality Assessment | AQA-7 | DAE-CoRe | Spearman Correlation | 85.20% | #1 of 9 | Archive leaderboard | report |
| Action Quality Assessment | AQA-7 | DAE-MLP | Spearman Correlation | 82.58% | #3 of 9 | Archive leaderboard | report |
| Action Quality Assessment | JIGSAWS | DAE-CoRe | Spearman Correlation | 0.86 | #3 of 5 | Archive leaderboard | report |
| Action Quality Assessment | JIGSAWS | DAE-MT | Spearman Correlation | 0.76 | #4 of 5 | Archive leaderboard | report |
| Action Quality Assessment | JIGSAWS | DAE-MLP | Spearman Correlation | 0.72 | #5 of 5 | Archive leaderboard | report |
| Action Quality Assessment | MTL-AQA | DAE-CoRe | Spearman Correlation | 95.89 | #4 of 21 | Archive leaderboard | report |
| Action Quality Assessment | MTL-AQA | DAE-MT | Spearman Correlation | 94.52 | #7 of 21 | Archive leaderboard | report |
| Action Quality Assessment | MTL-AQA | DAE-MLP | Spearman Correlation | 92.31 | #13 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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