Interleaved Image Text

Interleaved image-text data, where images and text are naturally interwoven as in web pages or stories, is a rapidly growing area of research focusing on developing models that can understand and generate this multimodal content. Current efforts concentrate on creating large-scale datasets of this type and designing models, often based on large language models (LLMs) and incorporating techniques like multimodal attention and latent compression learning, to effectively process and generate interleaved image-text sequences. This research is significant because it advances multimodal understanding and generation capabilities, with applications ranging from improved video understanding and text-image generation to more natural and engaging storytelling systems.