The field of multimodal perception is rapidly advancing, with a focus on developing more effective and efficient methods for integrating and processing multiple forms of data, such as visible and infrared images. Researchers are exploring innovative approaches, including biologically inspired models, knowledge distillation, and cross-modal learning, to improve performance in various applications, including autonomous vehicles, robotics, and healthcare. Notable papers in this area include UNIV, which proposes a unified foundation model for infrared and visible modalities, and DistillMatch, which leverages knowledge distillation for multimodal image matching. Other noteworthy papers include LCMF, LEAF-Mamba, and HyPSAM, which introduce novel frameworks and techniques for multimodal learning and fusion. These advancements have the potential to significantly improve the accuracy and efficiency of multimodal perception systems, enabling more effective decision-making and action in a variety of contexts.