  Action quality assessment (AQA) is an active research problem in video-based
applications that is a challenging task due to the score variance per frame.
Existing methods address this problem via convolutional-based approaches but
suffer from its limitation of effectively capturing long-range dependencies.
With the recent advancements in Transformers, we show that they are a suitable
alternative to the conventional convolutional-based architectures.
Specifically, can transformer-based models solve the task of AQA by effectively
capturing long-range dependencies, parallelizing computation, and providing a
wider receptive field for diving videos? To demonstrate the effectiveness of
our proposed architectures, we conducted comprehensive experiments and achieved
a competitive Spearman correlation score of 0.9317. Additionally, we explore
the hyperparameters effect on the model's performance and pave a new path for
exploiting Transformers in AQA.
