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MyVLM: Personalizing VLMs for User-Specific Queries (2403.14599v1)

Published 21 Mar 2024 in cs.CV

Abstract: Recent large-scale vision-LLMs (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 LLM 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.

PDF HTML Abstract

Personalized Vision-LLMs

The paper introduces MyVLM, a methodology aimed at enhancing existing vision-LLMs (VLMs) by enabling them to handle personalized queries related to user-specific concepts. This work addresses the limitations of current VLMs, which typically possess generic knowledge without the ability to comprehend and integrate individual user contexts. MyVLM targets two main tasks: personalized image captioning and visual question-answering.

Methodology Overview

MyVLM operates without altering the core weights of pretrained VLMs, preserving their innate visual and linguistic capabilities. It employs two primary strategies:

  1. Concept Heads: To recognize personalized content, the approach involves augmenting VLMs with external concept heads. These heads are specialized binary classifiers designed to identify the presence of specific user-defined concepts within an image. For humans, a pretrained face recognition model identifies individuals, while for objects, a linear classifier trained on extracted CLIP embeddings is employed.
  2. Embedding Vectors: MyVLM introduces concept embeddings within the VLM’s intermediate feature space. These vectors guide the LLM to incorporate the personalized concept naturally into its output, aligning it with the provided image input. The optimization leverages a small set of examples, where augmentations and regularization techniques enhance generalization and mitigate context leakage during personalization.

Experimental Implementation

MyVLM was tested on two prominent VLM architectures: BLIP-2 and LLaVA. Utilizing these frameworks, the paper demonstrates MyVLM's applicability extending to multiple VLM models. The personalization pipeline is trained with only a handful of images (3-5) per concept, showcasing the model's capacity for efficiency and adaptability.

Results

The effectiveness of MyVLM is illustrated through quantitative and qualitative evaluations, which emphasize improvement over traditional VLMs in recalling and integrating user-specific concepts within captions. The model's ability to precisely incorporate unique concepts, such as individual names or objects, into generated captions and visual queries shows marked advancement. Furthermore, the method showcases consistent results across two diverse VLM structures.

Quantitative Metrics

The model achieves high recall and image alignment in captioning tasks, surpassing baseline methods such as keyword-based replacements and LLM-guided interventions. Across both BLIP-2 and LLaVA models, MyVLM demonstrated significant recall of concept identifiers and improved textual similarity against ground truth captions.

Implications and Future Directions

The approach underscores a pivotal move toward more personalized and meaningful human-computer interaction within VLMs. By allowing models to understand user-specific contexts, MyVLM can enhance applications across personalized content creation, digital assistance, and more nuanced AI interactions. The introduction of external heads also allows for scalable capabilities, with potential expansion to include more diverse and complex concepts over time.

Future work may explore further optimization of concept embeddings and expanded datasets for additional personalization depths. Integrating insights from attention mechanisms could also enhance model robustness against context leakage and enable seamless adaptation to newer VLM architectures. Moreover, ethical considerations concerning privacy and data security should be key focal areas as personalization technology advances.

Overall, MyVLM represents a significant technical step towards individual-centric AI models, providing both methodological contributions and paving the way for further research into adaptive vision-language understanding.

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Authors (5)
  1. Yuval Alaluf (22 papers)
  2. Elad Richardson (18 papers)
  3. Sergey Tulyakov (108 papers)
  4. Kfir Aberman (46 papers)
  5. Daniel Cohen-Or (172 papers)
Citations (6)
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