Interleaved Multimodal

Interleaved multimodal research focuses on developing models that effectively process and integrate information from diverse sources like text, images, and audio within a single, unified representation. Current efforts concentrate on designing architectures, often leveraging large multimodal language models, that can handle complex, interwoven data streams and generate coherent outputs across modalities. This approach is significantly advancing capabilities in various applications, including information retrieval, graphic design, video understanding, and 3D model generation, by enabling more nuanced and contextually rich interpretations of multimodal data.