The field of multimodal fusion and perception is rapidly advancing, with a focus on developing innovative methods to integrate and process data from diverse sensors and sources. A key direction in this area is the development of robust and efficient fusion techniques that can handle challenging conditions such as noise, missing data, and varying sensor reliability. Recent works have proposed novel architectures and frameworks that leverage deep learning, diffusion models, and other advanced techniques to improve the accuracy and robustness of multimodal perception systems. Notable papers in this area include the Generative Diffusion Contrastive Network for multi-view clustering, DGFusion for depth-guided sensor fusion, and MSGFusion for multimodal scene graph-guided image fusion. These papers demonstrate state-of-the-art performance in various tasks such as clustering, segmentation, and fusion, and highlight the potential of multimodal fusion and perception techniques in applications such as autonomous driving, robotics, and computer vision. Other noteworthy papers include TUNI for real-time RGB-T semantic segmentation, CaR1 for camera-radar fusion, and 4DRadar-GS for self-supervised dynamic driving scene reconstruction. Overall, the field of multimodal fusion and perception is experiencing significant growth and innovation, with a focus on developing practical and effective solutions for real-world applications.