  The highly abstract nature of image aesthetics perception (IAP) poses
significant challenge for current multimodal large language models (MLLMs). The
lack of human-annotated multi-modality aesthetic data further exacerbates this
dilemma, resulting in MLLMs falling short of aesthetics perception
capabilities. To address the above challenge, we first introduce a
comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT)
dataset, which serves as the footstone for building multi-modality aesthetics
foundation models. Specifically, to align MLLMs with human aesthetics
perception, we construct a corpus-rich aesthetic critique database with 21,904
diverse-sourced images and 88K human natural language feedbacks, which are
collected via progressive questions, ranging from coarse-grained aesthetic
grades to fine-grained aesthetic descriptions. To ensure that MLLMs can handle
diverse queries, we further prompt GPT to refine the aesthetic critiques and
assemble the large-scale aesthetic instruction tuning dataset, i.e. AesMMIT,
which consists of 409K multi-typed instructions to activate stronger aesthetic
capabilities. Based on the AesMMIT database, we fine-tune the open-sourced
general foundation models, achieving multi-modality Aesthetic Expert models,
dubbed AesExpert. Extensive experiments demonstrate that the proposed AesExpert
models deliver significantly better aesthetic perception performances than the
state-of-the-art MLLMs, including the most advanced GPT-4V and
Gemini-Pro-Vision. Project homepage: https://yipoh.github.io/aes-expert/.
