Auto-encoding score distribution regression for action quality assessment
Neural Computing and Applications, ISSN: 1433-3058, Vol: 36, Issue: 2, Page: 929-942
2024
- 5Citations
- 10Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Assessing the quality of actions in videos is a challenging vision task, as the relationship between videos and action scores can be difficult to model. Consequently, extensive research has been conducted on action quality assessment (AQA) in the literature. Traditional AQA methods treat the problem as a regression task to learn the underlying mappings between videos and action scores. However, previous approaches overlook the presence of data uncertainty in AQA datasets. To address aleatoric uncertainty, we have developed a plug-and-play module called distribution auto-encoder (DAE). DAE encodes videos into distributions and utilizes the reparameterization trick to sample scores, which enables a more accurate mapping between videos and scores. Additionally, we use a likelihood loss to learn the uncertainty parameters. We have evaluated our approach on publicly available datasets, and extensive experiments demonstrate that DAE achieves state-of-the-art performance with the Spearman’s correlation metric of 82.58%, 92.32%, and 76.00% on the AQA-7, MTL-AQA, and JIGSAWSS datasets, respectively. Furthermore, plug-and-play experiments also demonstrate the extensibility of DAE. Our code is available at https://github.com/InfoX-SEU/DAE-AQA .
Bibliographic Details
Springer Science and Business Media LLC
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