AI RESEARCH
Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models
arXiv CS.CV
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ArXi:2605.15755v1 Announce Type: new Multimodal large language models (MLLMs) can produce fluent artwork emotion explanations, but they often suffer from attribute flooding: they enumerate many visible formal attributes without identifying which cues actually the affective judgment. We. therefore. formulate artwork emotion understanding as Attribute-Grounded Selective Reasoning (AGSR), where predefined formal attributes serve as evidence units and only emotionally operative attributes should enter the final interpretation. To make this problem measurable, we extend EmoArt, originally.