Authors: Ruixiang Jiang, Changwen Chen
Abstract: We present the first study on how Multimodal LLMs’ (MLLMs) reasoning ability
shall be elicited to evaluate the aesthetics of artworks. To facilitate this
investigation, we construct MM-StyleBench, a novel high-quality dataset for
benchmarking artistic stylization. We then develop a principled method for
human preference modeling and perform a systematic correlation analysis between
MLLMs’ responses and human preference. Our experiments reveal an inherent
hallucination issue of MLLMs in art evaluation, associated with response
subjectivity. ArtCoT is proposed, demonstrating that art-specific task
decomposition and the use of concrete language boost MLLMs’ reasoning ability
for aesthetics. Our findings offer valuable insights into MLLMs for art and can
benefit a wide range of downstream applications, such as style transfer and
artistic image generation. Code available at
https://github.com/songrise/MLLM4Art.
Source: http://arxiv.org/abs/2501.09012v1
