Personalized Visual Instruction Tuning
Authors: name: Renjie Pi, name: Jianshu Zhang, name: Tianyang Han, name: Jipeng Zhang, name: Rui Pan, name: Tong Zhang

Recent advancements in multimodal large language models (MLLMs) have
demonstrated significant progress; however, these models exhibit a notable
limitation, which we refer to as "face blindness". Specifically, 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, such as tailored visual assistants on mobile
devices, or domestic robots that need to recognize members of the family. In
this paper, we introduce Personalized Visual Instruction Tuning (PVIT), 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, image generation models, and (multi-modal) large language models. To
evaluate the personalized potential of MLLMs, we present a benchmark called
P-Bench, 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.