个性化工具包：无需训练的大型视觉语言模型个性化
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
AI总结：
本文提出无需训练的LVLM个性化方法，通过预训练视觉基础模型提取特征，结合检索增强生成技术实现多概念个性化，适用于图像和视频。
摘要：
大型视觉语言模型（LVLMs）的个性化涉及定制模型以识别特定用户或对象实例并生成上下文相关的响应。现有方法依赖于为每个项目耗时的训练，使其在实际部署中不可行，这反映在当前的个性化基准测试中仅限于对象中心的单一概念评估。本文提出了一种新的无需训练的LVLM个性化方法，称为\ours。我们引入了一个全面的现实世界基准，以严格评估个性化任务的各个方面。\ours利用预训练的视觉基础模型提取独特特征，应用检索增强生成（RAG）技术来识别视觉输入中的实例，并采用视觉提示策略来指导模型输出。我们的模型无关视觉工具包实现了在图像和视频上高效且灵活的多概念个性化，而无需任何额外训练。我们实现了最先进的结果，超过了现有基于训练的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
