Unified Discrete Diffusion for Simultaneous Vision-Language Generation

Minghui Hu, Chuanxia Zheng, Zuopeng Yang, Tat-Jen Cham, Heliang Zheng, Chaoyue Wang, Dacheng Tao, Ponnuthurai N. Suganthan

Keywords: Multi-modal, Image generation, Image Caption.

Abstract: 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.

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TL;DR: We proposed Unified Discrete Denoising Diffusion model, which allows us to construct a joint vision-language probability distribution, leading to a capability of simultaneously generating cross-domain results.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2211.14842/code)