Action Quality Assessment (AQA) is pivotal for quantifying actions across
domains like sports and medical care. Existing methods often rely on
pre-trained backbones from large-scale action recognition datasets to boost
performance on smaller AQA datasets. However, this common strategy yields
suboptimal results due to the inherent struggle of these backbones to capture
the subtle cues essential for AQA. Moreover, fine-tuning on smaller datasets
risks overfitting. To address these issues, we propose Coarse-to-Fine
Instruction Alignment (CoFInAl). Inspired by recent advances in large language
model tuning, CoFInAl aligns AQA with broader pre-trained tasks by
reformulating it as a coarse-to-fine classification task. Initially, it learns
grade prototypes for coarse assessment and then utilizes fixed sub-grade
prototypes for fine-grained assessment. This hierarchical approach mirrors the
judging process, enhancing interpretability within the AQA framework.
Experimental results on two long-term AQA datasets demonstrate CoFInAl achieves
state-of-the-art performance with significant correlation gains of 5.49% and
3.55% on Rhythmic Gymnastics and Fis-V, respectively. Our code is available at
this https URL

CoFInAl 提出了一种层级方法，将动作质量评估（AQA）改造为一个粗粒度到细粒度分类任务，通过与庞大的预训练任务对齐，以捕捉 AQA 中的微妙信号，实验结果在两个长期 AQA 数据集上取得了最先进的性能。

CoFInAl：利用粗到细的指令对鉴定行动质量进行增强

CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine  Instruction Alignment

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 this https URL

通过增加额外的预训练阶段，我们引入了 PECoP 这一创新的参数高效的连续预训练框架，并通过自监督学习来减少领域转变对于迁移学习的影响。我们通过 3D 适配器，只更新适配器模块参数的方式，展示了 PECoP 提升最新的 AQA 方法（MUSDL、CoRe 和 TSA）在 JIGSAWS、MTL-AQA 和 FineDiving 基准数据集上的表现，以及超过同类研究的新帕金森病数据集 PD4T 表现（提升 3.56%）。