The field of computer vision is moving towards the development of more efficient and accurate architectures for multimodal fusion and visual representation. Researchers are exploring the potential of combining different approaches, such as convolutional neural networks (CNNs) and state space models (SSMs), to leverage their respective strengths and overcome their limitations. This has led to the creation of innovative hybrid architectures that can capture both local and global features, while maintaining computational efficiency. Notable papers in this area include: CSFMamba, which proposes a cross-state fusion network for multimodal remote sensing image classification. Mamba-CNN, which integrates a lightweight Mamba-inspired SSM gating mechanism into a hierarchical convolutional backbone for facial beauty prediction. VCMamba, which bridges convolutions with multi-directional Mamba for efficient visual representation.