MyVLM: Personalizing VLMs for User-Specific Queries
This work addresses the limitation of VLMs in handling user-specific concepts, enabling personalized applications like image captioning and visual question-answering, representing an incremental step in model personalization.
The paper tackles the problem of personalizing vision-language models (VLMs) to understand user-specific concepts, such as recognizing individuals in images and describing their activities, by augmenting VLMs with external concept heads and learning new concept embeddings, achieving generalization to unseen images while preserving model behavior on unrelated inputs.
Recent large-scale vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and generating textual descriptions for visual content. However, these models lack an understanding of user-specific concepts. In this work, we take a first step toward the personalization of VLMs, enabling them to learn and reason over user-provided concepts. For example, we explore whether these models can learn to recognize you in an image and communicate what you are doing, tailoring the model to reflect your personal experiences and relationships. To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image. Having recognized the concept, we learn a new concept embedding in the intermediate feature space of the VLM. This embedding is tasked with guiding the language model to naturally integrate the target concept in its generated response. We apply our technique to BLIP-2 and LLaVA for personalized image captioning and further show its applicability for personalized visual question-answering. Our experiments demonstrate our ability to generalize to unseen images of learned concepts while preserving the model behavior on unrelated inputs.