Personalization Toolkit: Training Free Personalization Of Large Vision Language Models

Abstract

arXiv:2502.02452v4 Announce Type: replace Abstract: Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate contextually tailored responses. Existing approaches rely on time-consuming training for each item, making them impractical for real-world deployment, as reflected in current personalization benchmarks limited to object-centric single-concept evaluations. In this paper, we present a novel training-free approach to LVLM personalization called \ours. We introduce a comprehensive, real-world benchmark designed to rigorously evaluate various aspects of the personalization task. \ours leverages pre-trained vision foundation models to extract distinctive features, applies retrieval-augmented generation (RAG) techniques to identify instances within visual inputs, and employs visual prompting strategies to guide model outputs. Our model-agnostic vision toolkit enables efficient and flexible multi

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