Yo'LLaVA: Your Personalized Language and Vision Assistant
This addresses the limitation of generic LMMs for users needing AI assistants that recognize and discuss personalized subjects, representing a novel task rather than an incremental improvement.
The paper tackles the problem of personalizing Large Multimodal Models (LMMs) to handle specific subjects like a user's pet, proposing Yo'LLaVA which embeds personalized subjects into latent tokens from example images, resulting in more efficient learning with fewer tokens and better visual attribute encoding compared to baselines like LLaVA.
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).