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Personalized Large Vision-Language Models
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The personalization model has gained significant attention in image generation yet remains underexplored for large vision-language models (LVLMs). Beyond generic ones, with personalization, LVLMs handle interactive dialogues using 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 to continuously add new concepts during a dialogue without incurring additional costs, which significantly enhances the practicality. 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 recognizes 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.
Forward citations
Cited by 4 Pith papers
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Personalize Your Large Vision-language Models With In-context Prompt Tuning
ICPT converts a few reference images of a personalized concept into an adaptive-length visual prompt plus a label embedding, letting a frozen LVLM add and reason about multiple concepts on the fly.
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HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes
A hierarchical benchmark for multimodal models on human-centric visual understanding finds frontier models average under 60% and miss question-uncued visual evidence, with test-time scaling helping only marginally.
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ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
A personalized multimodal assistant trained with knowledge graphs and chain-of-thought QA can reason about relations between a user's concepts, beating prior recognition-only personalization methods.
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DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition
DRC disentangles style and semantics with a dual-tower attention module and re-composes them as latent instructions, improving personalized sticker and movie poster generation over the Pigeon baseline on style metrics...
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