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Unified Discrete Diffusion for Simultaneous Vision-Language Generation
Minghui HU · Chuanxia Zheng · Zuopeng Yang · Tat-Jen Cham · Heliang Zheng · Chaoyue Wang · Dacheng Tao · Ponnuthurai Suganthan
Keywords: [ Generative models ] [ Image Caption. ] [ image generation ] [ multi-modal ]
The recently developed discrete diffusion model performs extraordinarily well in generation tasks, especially in the text-to-image task, showing great potential for modeling multimodal signals. In this paper, we leverage these properties and present a unified multimodal generation model, which can perform text-based, image-based, and even vision-language simultaneous generation using a single model. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified Markov transition matrix and a unified objective. Moreover, we design a multimodal mutual attention module to highlight the inter-modal linkages, which is vital for multimodal generation. Extensive experiments indicate that our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.