The article discusses Chameleon, a novel approach to multimodal learning that uses a fully token-based early-fusion model. This model uniquely allows for interleaved encoding of text and image tokens, facilitating seamless reasoning and generation across modalities. Chameleon distinguishes itself from late-fusion methods that treat images and text separately, by forming a unified token space. The article also highlights relevant works that laid the groundwork for token-based approaches, addressing both the benefits and challenges inherent to this representation learning strategy.
Chameleon innovatively uses a fully token-based early-fusion model for multimodal learning, allowing seamless reasoning over interleaved text and image sequences without separate modality components.
With a fully token-based early-fusion model, Chameleon addresses representation learning challenges while facilitating integrated interactions between text and images, differing from existing late-fusion models.
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