RIS-FUSION: Rethinking Text-Driven Infrared and Visible Image Fusion from the Perspective of Referring Image Segmentation

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.

GRASP: Geospatial pixel Reasoning viA Structured Policy learning

Geospatial pixel reasoning is a nascent remote-sensing task that aims to generate segmentation masks directly from natural-language instructions. Prevailing MLLM-based systems co-train a language model and a mask decoder with dense pixel supervision, which is expensive and often weak on out-of-domain (OOD) data. We introduce GRASP, a structured policy-learning framework. In our design, a multimodal large language model first emits task-relevant bounding boxes and positive points from a vision-language instruction. These outputs are then passed to a pre-trained segmentation model, which consumes them as prompts to generate the final mask. Instead of supervised fine-tuning, we optimize the system purely with reinforcement learning: the model is trained solely with GRPO, guided by format rewards and accuracy rewards computed on boxes and points (no mask supervision). This leverages strong priors in foundation models, minimizes trainable parameters, and enables learning from inexpensive annotations. We additionally curate GRASP-1k, which contains reasoning-intensive queries, detailed reasoning traces, and fine-grained segmentation annotations. Evaluations on both in-domain and out-of-domain test sets show state-of-the-art results: about 4% improvement in-domain and up to 54% on OOD benchmarks. The experiment results evidence our model's robust generalization and demonstrate that complex geospatial segmentation behaviors can be learned via RL from weak spatial cues. Code and the dataset will be released open-source.

HSFusion: A high-level vision task-driven infrared and visible image fusion network via semantic and geometric domain transformation

Infrared and visible image fusion has been developed from vision perception oriented fusion methods to strategies which both consider the vision perception and high-level vision task. However, the existing task-driven methods fail to address the domain gap between semantic and geometric representation. To overcome these issues, we propose a high-level vision task-driven infrared and visible image fusion network via semantic and geometric domain transformation, terms as HSFusion. Specifically, to minimize the gap between semantic and geometric representation, we design two separate domain transformation branches by CycleGAN framework, and each includes two processes: the forward segmentation process and the reverse reconstruction process. CycleGAN is capable of learning domain transformation patterns, and the reconstruction process of CycleGAN is conducted under the constraint of these patterns. Thus, our method can significantly facilitate the integration of semantic and geometric information and further reduces the domain gap. In fusion stage, we integrate the infrared and visible features that extracted from the reconstruction process of two seperate CycleGANs to obtain the fused result. These features, containing varying proportions of semantic and geometric information, can significantly enhance the high level vision tasks. Additionally, we generate masks based on segmentation results to guide the fusion task. These masks can provide semantic priors, and we design adaptive weights for two distinct areas in the masks to facilitate image fusion. Finally, we conducted comparative experiments between our method and eleven other state-of-the-art methods, demonstrating that our approach surpasses others in both visual appeal and semantic segmentation task.