PECoP: Parameter Efficient Continual Pretraining for Action Quality
  Assessment
Amirhossein Dadashzadeh Shuchao Duan Alan Whone Majid Mirmehdi
The limited availability of labelled data in Action Quality Assessment (AQA),
has forced previous works to fine-tune their models pretrained on large-scale
domain-general datasets. This common approach results in weak generalisation,
particularly when there is a significant domain shift. We propose a novel,
parameter efficient, continual pretraining framework, PECoP, to reduce such
domain shift via an additional pretraining stage. In PECoP, we introduce
3D-Adapters, inserted into the pretrained model, to learn spatiotemporal,
in-domain information via self-supervised learning where only the adapter
modules' parameters are updated. We demonstrate PECoP's ability to enhance the
performance of recent state-of-the-art methods (MUSDL, CoRe, and TSA) applied
to AQA, leading to considerable improvements on benchmark datasets, JIGSAWS
($\uparrow6.0\%$), MTL-AQA ($\uparrow0.99\%$), and FineDiving
($\uparrow2.54\%$). We also present a new Parkinson's Disease dataset, PD4T, of
real patients performing four various actions, where we surpass
($\uparrow3.56\%$) the state-of-the-art in comparison. Our code, pretrained
models, and the PD4T dataset are available at https://github.com/Plrbear/PECoP.