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 have demonstrated significant progress%3B however%2C these models exhibit a notable limitation%2C which we refer to as face blindness. Specifically%2C they can engage in general conversations but fail to conduct personalized dialogues targeting at specific individuals. This deficiency hinders the application of MLLMs in personalized settings%2C such as tailored visual assistants on mobile devices%2C or domestic robots that need to recognize members of the family. In this paper%2C we introduce Personalized Visual Instruction Tuning (PVIT)%2C a novel data curation and training framework designed to enable MLLMs to identify target individuals within an image and engage in personalized and coherent dialogues. Our approach involves the development of a sophisticated pipeline that autonomously generates training data containing personalized conversations. This pipeline leverages the capabilities of various visual experts%2C image generation models%2C and (multi-modal) large language models. To evaluate the personalized potential of MLLMs%2C we present a benchmark called P-Bench%2C which encompasses various question types with different levels of difficulty. The experiments demonstrate a substantial personalized performance enhancement after fine-tuning with our curated dataset.?quality=80&w=800)
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