AesMind: Harnessing Multimodal Large Language Models for Neuroaesthetic Understanding and Assessmen

Recent advances in multimodal large language models (MLLMs) have significantly enhanced the interpretability and generalization of image aesthetic assessment (IAA). However, existing methods still predominantly evaluate aesthetics through either a single holistic score or a generic critique, failing to provide fine-grained attribute ratings and structured multidimensional reasoning. To address these limitations, we draw upon neuroaesthetics, which explores the cognitive and neural mechanisms of aesthetic appreciation, to establish a comprehensive aesthetic experience taxonomy encompassing the sensory-motor, emotion-valuation, and knowledge-meaning systems. Based on this theoretical framework, we first introduce NeuroAes, a large-scale dataset specifically tailored for computational photographic NeuroAesthetics, which comprises 25K images, annotated with over 1.2M fine-grained neuroaesthetic labels across 48 subcategories and over 2.25M human ratings spanning the valence-arousal-dominance (VAD) dimensions for both aesthetic and evoked emotions. In particular, NeuroAes is partitioned into a 5K-image NeuroAes-Bench for comprehensive aesthetic evaluation and a 20K-image NeuroAes-Data for large-scale training. Building upon this foundation, we develop AesMind, an all-in-one cognitive aesthetic MLLM that unifies perception, assessment, and description capabilities through a progressive three-stage training paradigm of skill harnessing, supervised fine-tuning (SFT), and group relative policy optimization (GRPO). Extensive experimental results demonstrate that AesMind achieves state-of-the-art performance across all benchmark tasks. Notably, it exhibits superior continuous-emotion regression, unprecedented cross-dataset generalization, and the unique capability to give aesthetic improvement suggestions, proving its ability to deeply understand photographic aesthetics rather than merely reciting superficial visual cues. Both NeuroAes and AesMind will be released to facilitate further research.