RIS-FUSION: 重新思考基于文本的红外和可见图像融合：从指代图像分割的角度
RIS-FUSION: Rethinking Text-Driven Infrared and Visible Image Fusion from the Perspective of Referring Image Segmentation
AI总结：
RIS-FUSION通过联合优化融合与指代图像分割，提升基于文本的红外和可见图像融合性能，实现mIoU指标的显著提升。
摘要：
基于文本的红外和可见图像融合已引起关注，因为它能够通过自然语言指导融合过程。然而，现有方法缺乏一个目标一致的任务来监督和评估输入文本对融合结果的贡献效果。我们发现，指代图像分割（RIS）和文本驱动的融合共享一个共同的目标：突出文本所指的对象。受此启发，我们提出了RIS-FUSION，一种级联框架，通过联合优化统一融合和RIS。其核心是LangGatedFusion模块，该模块将文本特征注入融合骨干网络以增强语义对齐。为了支持多模态指代图像分割任务，我们引入了MM-RIS，一个大规模基准，包含12.5k训练样本和3.5k测试三元组，每个三元组由红外-可见图像对、分割掩码和指代表达组成。大量实验表明，RIS-FUSION在mIoU上实现了最先进的性能，比现有方法高出超过11%。代码和数据集将在https://github.com/SijuMa2003/RIS-FUSION上发布。
英文摘要：
Text-driven infrared and visible image fusion has gained attention for enabling natural language to guide the fusion process. However, existing methods lack a goal-aligned task to supervise and evaluate how effectively the input text contributes to the fusion outcome. We observe that referring image segmentation (RIS) and text-driven fusion share a common objective: highlighting the object referred to by the text. Motivated by this, we propose RIS-FUSION, a cascaded framework that unifies fusion and RIS through joint optimization. At its core is the LangGatedFusion module, which injects textual features into the fusion backbone to enhance semantic alignment. To support multimodal referring image segmentation task, we introduce MM-RIS, a large-scale benchmark with 12.5k training and 3.5k testing triplets, each consisting of an infrared-visible image pair, a segmentation mask, and a referring expression. Extensive experiments show that RIS-FUSION achieves state-of-the-art performance, outperforming existing methods by over 11% in mIoU. Code and dataset will be released at https://github.com/SijuMa2003/RIS-FUSION.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
