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
*** Want to know the score of a Dive at the ongoing Olympics, even before the judges' decision? Try out our AI Olympics Judge ***