MG-IQA trains vision-language models with attribute-aware RL2R and a multi-dimensional Thurstone reward model to jointly predict overall quality and fine-grained attributes, reporting 2.1% average SRCC gains on eight IQA benchmarks.
After analyzing each attribute, provide an overall quality assessment that synthesizes your findings
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Multi-Granularity Reasoning for Image Quality Assessment via Attribute-Aware Reinforcement Learning to Rank
MG-IQA trains vision-language models with attribute-aware RL2R and a multi-dimensional Thurstone reward model to jointly predict overall quality and fine-grained attributes, reporting 2.1% average SRCC gains on eight IQA benchmarks.