RTF: Recursive TransFusion for Multi-Modal Image Synthesis

Multi-modal image synthesis is crucial for obtaining complete modalities due to the imaging restrictions in reality. Current methods, primarily CNN-based models, find it challenging to extract global representations because of local inductive bias, leading to synthetic structure deformation or color distortion. Despite the significant global representation ability of transformer in capturing long-range dependencies, its huge parameter size requires considerable training data. Multi-modal synthesis solely based on one of the two structures makes it hard to extract comprehensive information from each modality with limited data. To tackle this dilemma, we propose a simple yet effective Recursive TransFusion (RTF) framework for multi-modal image synthesis. Specifically, we develop a TransFusion unit to integrate local knowledge extracted from the individual modality by connecting a CNN-based local representation block (LRB) and a transformer-based global fusion block (GFB) via a feature translating gate (FTG). Considering the numerous parameters introduced by the transformer, we further unfold a TransFusion unit with recursive constraint repeatedly, forming recursive TransFusion (RTF), which progressively extracts multi-modal information at different depths. Our RTF remarkably reduces network parameters while maintaining superior performance. Extensive experiments validate our superiority against the competing methods on multiple benchmarks. The source code will be available at https://github.com/guoliangq/RTF.