UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and
  Benchmark
Zhaokun Zhou Qiulin Wang Bin Lin Yiwei Su Rui Chen Xin Tao Amin Zheng Li Yuan Pengfei Wan Di Zhang
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.