2022/11/27 by Minghui Hu, Chuanxia Zheng, Hu, Minghui +13 · 5 citations
Computer Science · #Multimodal Machine Learning Applications #Advanced Image and Video Retrieval Techniques #Domain Adaptation and Few-Shot Learning
paper · pdf · doi:10.48550/arxiv.2211.14842
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.