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arXiv · 2211.14842

Unified Discrete Diffusion for Simultaneous Vision-Language Generation

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

The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks using a single model, performing text-based, image-based, and even vision-language simultaneous generation. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Moreover, we design a mutual attention module with fused embedding layer and a unified objective function to emphasise the inter-modal linkages, which are vital for multi-modality 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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Minghui Hu, Chuanxia Zheng, Heliang Zheng, Tat-Jen Cham, Chaoyue Wang, Zuopeng Yang, Dacheng Tao, Ponnuthurai N. Suganthan. 2022-11-27. Unified Discrete Diffusion for Simultaneous Vision-Language Generation. https://arxiv.org/abs/2211.14842

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