TaiChi‐AQA: A Dataset and Framework for Action Quality Assessment and Visual Analysis
Dejin Wang, Fengyan Lin, Kexin Zhu, Z. Chen · IET Computer Vision · 2025
ABSTRACT Action Quality Assessment (AQA) has become an advanced technology applied in various domains. However, most existing datasets focus on sports events, such as the Olympics, whereas datasets tailored for daily exercise activities remain scarce. Additionally, many of these datasets are unsuitable for direct application in AQA tasks. To address these limitations, we constructed a new AQA dataset, TaiChi‐AQA, which includes detailed scoring annotations. Our dataset comprises 1313 Tai Chi action videos and features a comprehensive set of fine‐grained labels, including action labels, action descriptions and frame‐level perspective information. To validate the effectiveness of TaiChi‐AQA, we systematically evaluated it using a variety of popular AQA methods. We also propose a straightforward yet effective module that integrates a multi‐head attention mechanism with a gated multilayer perceptron (gMLP). This module is combined with the distributed autoencoder (DAE) framework. Extensive experiments demonstrate that our method achieves state‐of‐the‐art performance on the TaiChi‐AQA dataset. The dataset are publicly available at https://github.com/mlxger/TaiChi‐AQA .