Image Aesthetics Assessment via Learnable Queries
Zhiwei Xiong Yunfan Zhang Zhiqi Shen Peiran Ren Han Yu
Image aesthetics assessment (IAA) aims to estimate the aesthetics of images.
Depending on the content of an image, diverse criteria need to be selected to
assess its aesthetics. Existing works utilize pre-trained vision backbones
based on content knowledge to learn image aesthetics. However, training those
backbones is time-consuming and suffers from attention dispersion. Inspired by
learnable queries in vision-language alignment, we propose the Image Aesthetics
Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries
to extract aesthetic features from pre-trained image features obtained from a
frozen image encoder. Extensive experiments on real-world data demonstrate the
advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1%
in terms of SRCC and PLCC, respectively.