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).

本文介绍了将个性化主题嵌入到一组潜在令牌中的 Yo'LLaVA 方法，通过少量示例图像有效地学习并更有效地编码视觉属性，用于实现 Large Multimodal Models（LMMs）与特定主题的对话。