Personalized Visual Instruction Tuning (2410.07113v1)

Abstract: Recent advancements in multimodal LLMs (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) LLMs. 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.

• The paper proposes a novel framework that fine-tunes multimodal models to overcome 'face blindness' and enable personalized dialogues.
• It employs a comprehensive methodology involving visual concept curation, textual fusion, and data generation through LLMs for training.
• Benchmark evaluations via P-Bench demonstrate significant improvements in individual recognition and personalized response generation.

Personalized Visual Instruction Tuning: Enhancing Multimodal LLMs for Personalized Dialogue

The paper presents Personalized Visual Instruction Tuning (PVIT), a framework that addresses significant limitations in current Multimodal LLMs (MLLMs). These models, while proficient in general conversation tasks, exhibit a deficiency termed "face blindness," impeding their ability to engage in personalized dialogues. PVIT introduces a novel approach to equip MLLMs with the capability to recognize individuals within images and engage in customized conversations, a critical requirement for applications such as tailored visual assistants and domestic robots.

Overview

The researchers identify the problem of personalization in MLLMs and propose PVIT, which leverages both data curation and a sophisticated training framework. The approach involves creating a framework to generate training data containing personalized conversations using visual experts, image generation models, and LLMs. This data is then used to fine-tune MLLMs, significantly enhancing their ability to conduct personalized dialogues.

Methodology

1. Data Curating Framework: The process involves three distinct phases:
   • Visual Concept Curation: Extracts visual concepts of individuals from scene images.
   • Textual Information Extraction and Fusion: Converts these visual concepts into both individual and scene-level textual descriptions.
   • PVIT Dataset Generation: Employs LLMs to generate diverse personalized QA pairs utilizing reasoning and instruction-following capabilities.
2. Personalized Visual Instruction Tuning: MLLMs are fine-tuned using the curated dataset, optimizing them to produce personalized responses without requiring additional tuning per individual.
3. Benchmark Evaluation: A benchmark named P-Bench is introduced to evaluate the personalized potential of MLLMs, comprising various question types with differing complexities.

Results

The experiments indicate substantial performance enhancements in personalization tasks following fine-tuning with PVIT. The model trained using this framework exhibits improved recognition of individuals and produces coherent personalized responses.

Implications and Future Prospects

The introduction of PVIT offers significant practical implications in the field of personalized AI applications. By enabling MLLMs to generalize across arbitrary individuals without additional training, the framework addresses the challenge of inflexibility found in prior methods. Furthermore, the methodology for data generation and model evaluation establishes a robust foundation for future developments in personalized AI dialogues.

Moving forward, the research may explore expanding the scope of personalized information beyond basic introductions, incorporating richer character and behavioral data. Additionally, the application of PVIT in real-world scenarios could lead to enhancements in user-specific AI interactions, particularly in areas requiring nuanced personal engagement.

In conclusion, PVIT represents a significant advancement in overcoming the limitations of current MLLMs in personalized dialogue, setting a precedent for future exploration and practical application in AI-driven personalization tasks.