Ego: Embedding-Guided Personalization of Vision-Language Models

TL;DR

This paper introduces an efficient method for personalizing vision-language models by extracting and utilizing internal attention-based visual tokens to recall specific concepts, enhancing personalized AI assistant capabilities.

Contribution

The proposed approach leverages the model's internal attention to personalize vision-language models without additional training, improving scalability and deployment efficiency.

Findings

Strong performance gains over SOTA methods

Effective across single, multi-concept, and video personalization

Minimal personalization overhead

Abstract

AI assistants that support humans in daily life are becoming increasingly feasible, driven by the rapid advancements in multimodal language models. A key challenge lies in overcoming the generic nature of these models to deliver personalized experiences. Existing approaches to personalizing large vision language models often rely on additional training stages, which limit generality and scalability, or on engineered pipelines with external pre-trained modules, which hinder deployment efficiency. In this work, we propose an efficient personalization method that leverages the model's inherent ability to capture personalized concepts. Specifically, we extract visual tokens that predominantly represent the target concept by utilizing the model's internal attention mechanisms. These tokens serve as a memory of that specific concept, enabling the model to recall and describe it when it appears in test images. We conduct a comprehensive and unified evaluation of our approach and SOTA methods across various personalization settings including single-concept, multi-concept, and video personalization, demonstrating strong performance gains with minimal personalization overhead.