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

This repository contains MTL-AQA dataset code introduced in the above paper. If you find this dataset or code useful, please consider citing:

@inproceedings{mtlaqa,
  title={What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment},
  author={Parmar, Paritosh and Tran Morris, Brendan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={304--313},
  year={2019}
}

Oct 2024: We have developed a new approach, NeuroSymbolic AQA, that builds upon this approach, but also analyses and scores using Professional Rules-based programs. It is Comprehensive and Explainable AQA which can generate Full Performance Reports for Actionable Insights!!! We encourage you to checkout [Code, Rules-based Programs, Dataset] [Demo] [Full Paper]

You are welcome to continue using this project, as it will still be maintained alongside the new approach!

Fine-grained Exercise Action Quality Assessment: Self-Supervised Pose-Motion Contrastive Approaches for Fine-grained Action Quality Assessment (can be used for Diving as well!) Fitness-AQA dataset

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