Self-attention based Transformer has achieved great success in many computer
vision tasks. However, its application to video quality assessment (VQA) has
not been satisfactory so far. Evaluating the quality of in-the-wild videos is
challenging due to the unknown of pristine reference and shooting distortion.
This paper presents a co-trained Space-Time Attention network for the VQA
problem, termed StarVQA+. Specifically, we first build StarVQA+ by alternately
concatenating the divided space-time attention. Then, to facilitate the
training of StarVQA+, we design a vectorized regression loss by encoding the
mean opinion score (MOS) to the probability vector and embedding a special
token as the learnable variable of MOS, leading to better fitting of human's
rating process. Finally, to solve the data hungry problem with Transformer, we
propose to co-train the spatial and temporal attention weights using both
images and videos. Various experiments are conducted on the de-facto
in-the-wild video datasets, including LIVE-Qualcomm, LIVE-VQC, KoNViD-1k,
YouTube-UGC, LSVQ, LSVQ-1080p, and DVL2021. Experimental results demonstrate
the superiority of the proposed StarVQA+ over the state-of-the-art.

本文提出了一种基于自注意力机制的 Space-Time Attention 网络用于解决视频质量评估问题，通过联合训练空间和时间注意力权重来解决 Transformer 中的数据饥饿问题，并在实际测试中证明了其优越性。