Yo'LLaVA: Your Personalized Language and Vision Assistant

TL;DR

This paper introduces Yo'LLaVA, a method for personalizing large multimodal models to recognize and discuss specific subjects based on few example images, enhancing their contextual understanding and conversational capabilities.

Contribution

Yo'LLaVA is a novel approach that embeds personalized subjects into latent tokens, enabling LMMs to handle personalized queries with fewer examples and improved visual attribute encoding.

Findings

Yo'LLaVA learns personalized concepts efficiently with fewer tokens.

It encodes visual attributes more effectively than baseline prompting methods.

The approach improves conversational relevance about specific subjects.

Abstract

Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering). While broad, their knowledge remains generic (e.g., recognizing a dog), and they are unable to handle personalized subjects (e.g., recognizing a user's pet dog). Human reasoning, in contrast, typically operates within the context of specific subjects in our surroundings. For example, one might ask, "What should I buy for my dog's birthday?"; as opposed to a generic inquiry about "What should I buy for a dog's birthday?". Similarly, when looking at a friend's image, the interest lies in seeing their activities (e.g., "my friend is holding a cat"), rather than merely observing generic human actions (e.g., "a man is holding a cat"). In this paper, we introduce the novel task of personalizing LMMs, so that they can have conversations about a specific subject. We propose Yo'LLaVA, which learns to embed a personalized subject into a set of latent tokens given a handful of example images of the subject. Our qualitative and quantitative analyses reveal that Yo'LLaVA can learn the concept more efficiently using fewer tokens and more effectively encode the visual attributes compared to strong prompting baselines (e.g., LLaVA).