The authors propose the UNIAA framework to address the limitations of traditional IAA methods, which are typically constrained to a single dataset or task, restricting the universality and broader application.
UNIAA includes:
To obtain the UNIAA-LLaVA, the authors establish a low-cost IAA Dataset Conversion Paradigm (IDCP) to transform existing aesthetic datasets into a format suitable for MLLM fine-tuning.
Extensive experiments validate the effectiveness of UNIAA. UNIAA-LLaVA achieves competitive performance on all levels of UNIAA-Bench, compared with existing MLLMs. Specifically, it performs better than GPT-4V in aesthetic perception and even approaches the junior-level human. The authors find MLLMs have great potential in IAA, yet there remains plenty of room for further improvement.
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by Zhaokun Zhou... at arxiv.org 04-16-2024
https://arxiv.org/pdf/2404.09619.pdfDeeper Inquiries