The field of multimodal information processing is witnessing significant developments, with a focus on improving the accuracy and efficiency of image segmentation, inverse problems, and multimodal fusion. Researchers are exploring innovative approaches, such as integrating partial attention convolutions with Mamba architectures, regularized Schrödinger bridges, and flow matching paradigms, to address the limitations of existing methodologies. Noteworthy papers in this area include MPCM-Net, which proposes a multi-scale network for ground-based cloud image segmentation, and Regularized Schrödinger Bridge, which alleviates distortion and exposure bias in solving inverse problems. Additionally, papers such as FusionFM and OTCR are making significant contributions to multimodal image fusion and representation learning, respectively.