  As an alternative to expensive expert evaluation, Image Aesthetic Assessment
(IAA) stands out as a crucial task in computer vision. However, traditional IAA
methods are typically constrained to a single data source or task, restricting
the universality and broader application. In this work, to better align with
human aesthetics, we propose a Unified Multi-modal Image Aesthetic Assessment
(UNIAA) framework, including a Multi-modal Large Language Model (MLLM) named
UNIAA-LLaVA and a comprehensive benchmark named UNIAA-Bench. We choose MLLMs
with both visual perception and language ability for IAA and establish a
low-cost paradigm for transforming the existing datasets into unified and
high-quality visual instruction tuning data, from which the UNIAA-LLaVA is
trained. To further evaluate the IAA capability of MLLMs, we construct the
UNIAA-Bench, which consists of three aesthetic levels: Perception, Description,
and Assessment. Extensive experiments validate the effectiveness and
rationality of UNIAA. UNIAA-LLaVA achieves competitive performance on all
levels of UNIAA-Bench, compared with existing MLLMs. Specifically, our model
performs better than GPT-4V in aesthetic perception and even approaches the
junior-level human. We find MLLMs have great potential in IAA, yet there
remains plenty of room for further improvement. The UNIAA-LLaVA and UNIAA-Bench
will be released.
