Video quality assessment (VQA) has attracted growing attention in recent
years. While the great expense of annotating large-scale VQA datasets has
become the main obstacle for current deep-learning methods. To surmount the
constraint of insufficient training data, in this paper, we first consider the
complete range of video distribution diversity (\ie content, distortion,
motion) and employ diverse pretrained models (\eg architecture, pretext task,
pre-training dataset) to benefit quality representation. An Adaptive Diverse
Quality-aware feature Acquisition (Ada-DQA) framework is proposed to capture
desired quality-related features generated by these frozen pretrained models.
By leveraging the Quality-aware Acquisition Module (QAM), the framework is able
to extract more essential and relevant features to represent quality. Finally,
the learned quality representation is utilized as supplementary supervisory
information, along with the supervision of the labeled quality score, to guide
the training of a relatively lightweight VQA model in a knowledge distillation
manner, which largely reduces the computational cost during inference.
Experimental results on three mainstream no-reference VQA benchmarks clearly
show the superior performance of Ada-DQA in comparison with current
state-of-the-art approaches without using extra training data of VQA.

提出了一种 Ada-DQA 框架，通过利用多样的预训练模型来捕获所生成的与质量相关的特征，以及使用有标签的质量分数和学习的质量表示作为辅助监督信息，以引导相对较轻的 VQA 模型的训练，从而在知识蒸馏的方式下大大减少推理期间的计算成本。