Personalized Large Vision-Language Model

Chau Pham, Hoang Phan, David Doermann, Yunjie Tian

Keywords: Personalization model, Large Vision-Language Model

Abstract: The personalization model has gained significant attention in the field of image generation yet remains underexplored for large vision-language models (LVLMs). Beyond generic ones, with personalization, LVLMs handle interactive dialogues using clearly referential concepts (e.g., “Mike and Susan are talking.”) instead of the generic form (e.g., “a boy and a girl are talking.”), making the conversation more customizable and referentially friendly. In addition, PLVM is equipped with the ability of continuously adding new concepts during a dialogue without incurring additional costs, which significantly enhances the practicality. Basically, PLVM proposes Aligner, a pre-trained visual encoder to align referential concepts with the queried images. During the dialogues, it extracts features of reference images with these corresponding concepts and recognize them in the queried image, enabling personalization. We note that the computational cost and parameter count of the Aligner are negligible within the entire framework. With comprehensive qualitative and quantitative analyses, we reveal the effectiveness and superiority of PLVM.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1639