Personalization Toolkit: Training Free Personalization of Large Vision Language Models

• URL: http://arxiv.org/abs/2502.02452v1
• Date: Tue, 04 Feb 2025 16:19:20 GMT
• Title: Personalization Toolkit: Training Free Personalization of Large Vision Language Models
• Authors: Soroush Seifi, Vaggelis Dorovatas, Daniel Olmeda Reino, Rahaf Aljundi,
• Abstract summary: Large Vision Language Models (LVLMs) have potential to deliver personalized assistance by adapting to individual users' unique needs and preferences. Existing approaches rely on time-consuming test-time training for each user and object, rendering them impractical. This paper proposes a novel, training-free approach to LVLM personalization by leveraging pre-trained vision foundation models.
• Score: 11.026377387506216
• License:
• Abstract: Large Vision Language Models (LVLMs) have significant potential to deliver personalized assistance by adapting to individual users' unique needs and preferences. Personalization of LVLMs is an emerging area that involves customizing models to recognize specific object instances and provide tailored responses. However, existing approaches rely on time-consuming test-time training for each user and object, rendering them impractical. This paper proposes a novel, training-free approach to LVLM personalization by leveraging pre-trained vision foundation models to extract distinct features, retrieval-augmented generation (RAG) techniques to recognize instances in the visual input, and visual prompting methods. Our model-agnostic vision toolkit enables flexible and efficient personalization without extensive retraining. We demonstrate state-of-the-art results, outperforming conventional training-based approaches and establish a new standard for LVLM personalization.

Related papers

• Personalized Visual Instruction Tuning [30.677058613937067]
  multimodal large language models (MLLMs) can engage in general conversations but fail to conduct personalized dialogues targeting at specific individuals. This deficiency hinders the application of MLLMs in personalized settings, such as tailored visual assistants on mobile devices. We introduce Personalized Visual Instruction Tuning (PVIT), a novel data curation and training framework designed to enable MLLMs to identify target individuals within an image.
  arXiv  Detail & Related papers  (2024-10-09T17:46:53Z)
• PAD: Personalized Alignment of LLMs at Decoding-Time [10.347782385286582]
  This paper presents a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase. The Personalized Alignment at Decoding-time (PAD) framework decouples the text generation process from personalized preferences. PAD not only outperforms existing training-based alignment methods in terms of aligning with diverse preferences but also shows significant generalizability to preferences unseen during training.
  arXiv  Detail & Related papers  (2024-10-05T08:00:55Z)
• JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation [49.997839600988875]
  Existing personalization methods rely on finetuning a text-to-image foundation model on a user's custom dataset. We propose Joint-Image Diffusion (jedi), an effective technique for learning a finetuning-free personalization model. Our model achieves state-of-the-art generation quality, both quantitatively and qualitatively, significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.
  arXiv  Detail & Related papers  (2024-07-08T17:59:02Z)
• Enhancing Large Vision Language Models with Self-Training on Image Comprehension [131.14381425260706]
  We introduce Self-Training on Image (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference for image descriptions using unlabeled images. To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data.
  arXiv  Detail & Related papers  (2024-05-30T05:53:49Z)
• U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation [18.841473623776153]
  State-of-the-art personalization models tend to overfit the whole subject and cannot disentangle visual characteristics in pixel space. A novel decoupled self-augmentation strategy is proposed to generate target-related and non-target samples to learn user-specified visual attributes. Experiments on various kinds of visual attributes with SOTA personalization methods show the ability of the proposed method to mimic target visual appearance in novel contexts.
  arXiv  Detail & Related papers  (2024-03-29T15:20:34Z)
• Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond [87.1712108247199]
  Our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP) We develop a generic and personalization generative framework, that can handle a wide range of personalized needs. Our methodology enhances the capabilities of foundational language models for personalized tasks.
  arXiv  Detail & Related papers  (2024-03-15T20:21:31Z)
• When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities [60.5609416496429]
  The capability of large language models has been dramatically improved. Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted. By leveraging large language models as general-purpose interface, personalization systems may compile user requests into plans.
  arXiv  Detail & Related papers  (2023-07-31T02:48:56Z)
• SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification [84.05253637260743]
  We propose a new framework, named Semantic-guided Visual Adapting (SgVA), to extend vision-language pre-trained models. SgVA produces discriminative task-specific visual features by comprehensively using a vision-specific contrastive loss, a cross-modal contrastive loss, and an implicit knowledge distillation. State-of-the-art results on 13 datasets demonstrate that the adapted visual features can well complement the cross-modal features to improve few-shot image classification.
  arXiv  Detail & Related papers  (2022-11-28T14:58:15Z)
• Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning [97.28779163988833]
  Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling. We propose textitMOMETAS, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives.
  arXiv  Detail & Related papers  (2022-10-19T04:38:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.