Unified Discrete Diffusion for Simultaneous Vision-Language Generation

• URL: http://arxiv.org/abs/2211.14842v1
• Date: Sun, 27 Nov 2022 14:46:01 GMT
• Title: Unified Discrete Diffusion for Simultaneous Vision-Language Generation
• Authors: Minghui Hu, Chuanxia Zheng, Heliang Zheng, Tat-Jen Cham, Chaoyue Wang, Zuopeng Yang, Dacheng Tao, Ponnuthurai N. Suganthan
• Abstract summary: We present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.
• Score: 78.21352271140472
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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