  Image aesthetics assessment (IAA) is attracting wide interest with the
prevalence of social media. The problem is challenging due to its subjective
and ambiguous nature. Instead of directly extracting aesthetic features solely
from the image, user comments associated with an image could potentially
provide complementary knowledge that is useful for IAA. With existing
large-scale pre-trained models demonstrating strong capabilities in extracting
high-quality transferable visual and textual features, learnable queries are
shown to be effective in extracting useful features from the pre-trained visual
features. Therefore, in this paper, we propose MMLQ, which utilizes multi-modal
learnable queries to extract aesthetics-related features from multi-modal
pre-trained features. Extensive experimental results demonstrate that MMLQ
achieves new state-of-the-art performance on multi-modal IAA, beating previous
methods by 7.7% and 8.3% in terms of SRCC and PLCC, respectively.
