User Generated Content (UGC) videos are susceptible to complicated and variant degradations and contents, which prevents the existing blind video quality assessment (BVQA) models from good performance since the lack of the adapability of distortions and contents. To mitigate this, we propose a novel prior-augmented perceptual vision transformer (PriorFormer) for the BVQA of UGC, which boots its adaptability and representation capability for divergent contents and distortions. Concretely, we introduce two powerful priors, i.e., the content and distortion priors, by extracting the content and distortion embeddings from two pre-trained feature extractors. Then we adopt these two powerful embeddings as the adaptive prior tokens, which are transferred to the vision transformer backbone jointly with implicit quality features. Based on the above strategy, the proposed PriorFormer achieves state-of-the-art performance on three public UGC VQA datasets including KoNViD-1K, LIVE-VQC and YouTube-UGC.

对于用户生成内容（UGC）视频，现有的盲目视频质量评估（BVQA）模型无法很好地适应复杂和多样的退化和内容，因此我们提出了一种新颖的预先增强的感知视觉变换（PriorFormer）用于UGC的BVQA，该模型能够提升对不同内容和退化的适应性和表示能力，并在包括KoNViD-1K、LIVE-VQC和YouTube-UGC在内的三个公共UGC VQA数据集上取得了最先进的性能。

Priorformer: 一种具有内容和失真先验的用户生成内容问答方法