The field of multimodal learning and visual decoding is rapidly evolving, with a focus on developing more efficient and effective methods for processing and analyzing complex data. One of the key trends in this area is the use of transformer architectures and multi-level attention mechanisms to improve the accuracy and robustness of visual decoding models. Additionally, there is a growing interest in leveraging multi-modal inputs and weak supervision to enhance the performance of segmentation and parsing models. Another important direction is the development of unified frameworks that can handle multiple tasks and modalities, such as zero-shot visual decoding and multi-modal semantic segmentation. Notable papers in this area include VoxelFormer, which introduces a lightweight transformer architecture for multi-subject visual decoding, and OmniSegmentor, which proposes a flexible multi-modal learning framework for semantic segmentation. Other noteworthy papers include LSTC-MDA, which presents a unified framework for long-short term temporal convolution and mixed data augmentation, and UMind, which introduces a unified multitask network for zero-shot M/EEG visual decoding.