CtrlFuse: Mask-Prompt Guided Controllable Infrared and Visible Image Fusion
This addresses the need for interactive and adaptable image fusion in intelligent unmanned systems, offering a novel approach beyond existing rigid methods.
The authors tackled the problem of infrared and visible image fusion for enhanced perception in unmanned systems by proposing CtrlFuse, a controllable framework that uses mask prompts to dynamically guide fusion, achieving state-of-the-art results in both fusion controllability and segmentation accuracy, with the adapted task branch outperforming the original segmentation model.
Infrared and visible image fusion generates all-weather perception-capable images by combining complementary modalities, enhancing environmental awareness for intelligent unmanned systems. Existing methods either focus on pixel-level fusion while overlooking downstream task adaptability or implicitly learn rigid semantics through cascaded detection/segmentation models, unable to interactively address diverse semantic target perception needs. We propose CtrlFuse, a controllable image fusion framework that enables interactive dynamic fusion guided by mask prompts. The model integrates a multi-modal feature extractor, a reference prompt encoder (RPE), and a prompt-semantic fusion module (PSFM). The RPE dynamically encodes task-specific semantic prompts by fine-tuning pre-trained segmentation models with input mask guidance, while the PSFM explicitly injects these semantics into fusion features. Through synergistic optimization of parallel segmentation and fusion branches, our method achieves mutual enhancement between task performance and fusion quality. Experiments demonstrate state-of-the-art results in both fusion controllability and segmentation accuracy, with the adapted task branch even outperforming the original segmentation model.