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arXiv · 2502.02452

Personalization Toolkit: Training Free Personalization of Large Vision Language Models

Soroush Seifi·Vaggelis Dorovatas·Matteo Cassinelli·Fabien Despinoy·Daniel Olmeda Reino·Rahaf Aljundi
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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-concept personalization across both images and videos, without any additional training. We achieve state-of-the-art results, surpassing existing training-based methods.

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Soroush Seifi, Vaggelis Dorovatas, Matteo Cassinelli, Fabien Despinoy, Daniel Olmeda Reino, Rahaf Aljundi. 2025-02-04. Personalization Toolkit: Training Free Personalization of Large Vision Language Models. https://arxiv.org/abs/2502.02452

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