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Computer Science > Computation and Language

arXiv:2405.09818 (cs)
[Submitted on 16 May 2024]

Title:Chameleon: Mixed-Modal Early-Fusion Foundation Models

Authors:Chameleon Team
View a PDF of the paper titled Chameleon: Mixed-Modal Early-Fusion
Foundation Models, by Chameleon Team
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    Abstract:We present Chameleon, a family of early-fusion
    token-based mixed-modal models capable of understanding and
    generating images and text in any arbitrary sequence. We outline
    a stable training approach from inception, an alignment recipe,
    and an architectural parameterization tailored for the
    early-fusion, token-based, mixed-modal setting. The models are
    evaluated on a comprehensive range of tasks, including visual
    question answering, image captioning, text generation, image
    generation, and long-form mixed modal generation. Chameleon
    demonstrates broad and general capabilities, including
    state-of-the-art performance in image captioning tasks,
    outperforms Llama-2 in text-only tasks while being competitive
    with models such as Mixtral 8x7B and Gemini-Pro, and performs
    non-trivial image generation, all in a single model. It also
    matches or exceeds the performance of much larger models,
    including Gemini Pro and GPT-4V, according to human judgments on
    a new long-form mixed-modal generation evaluation, where either
    the prompt or outputs contain mixed sequences of both images and
    text. Chameleon marks a significant step forward in a unified
    modeling of full multimodal documents.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2405.09818 [cs.CL]
          (or arXiv:2405.09818v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2405.09818
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Armen Aghajanyan [view email]
[v1] Thu, 16 May 2024 05:23:41 UTC (26,721 KB)
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    View a PDF of the paper titled Chameleon: Mixed-Modal
    Early-Fusion Foundation Models, by Chameleon Team
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