Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM

• URL: http://arxiv.org/abs/2604.17233v1
• Date: Sun, 19 Apr 2026 03:35:25 GMT
• Title: Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM
• Abstract summary: We introduce P-MLLM, a profile-aware multimodal LLM that augments a frozen LLM with selective fusion modules for controlled visual integration.<n>Experiments on recent PIAA benchmarks show that P-MLLM achieves competitive zero-shot performance.
• Score: 40.55407144140813
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Personalized image aesthetics assessment (PIAA) aims to predict an individual user's subjective rating of an image, which requires modeling user-specific aesthetic preferences. Existing methods rely on historical user ratings for this modeling and therefore struggle when such data are unavailable. We address this zero-shot setting by using user profiles as contextual signals for personalization and adopting a profile-based personalization paradigm. We introduce P-MLLM, a profile-aware multimodal LLM that augments a frozen LLM with selective fusion modules for controlled visual integration. These modules selectively integrate visual information into the model's evolving hidden states during profile-conditioned reasoning, allowing visual information to be incorporated in a profile-aware manner. Experiments on recent PIAA benchmarks show that P-MLLM achieves competitive zero-shot performance and remains effective even with coarse profile information, highlighting the potential of profile-based personalization for zero-shot PIAA.

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