Visual State Space Model

Visual state space models (VSSMs) are a class of deep learning architectures designed to efficiently process sequential data, such as image patches, by leveraging the strengths of recurrent neural networks and latent variable models. Current research focuses on improving VSSMs' performance and efficiency in computer vision tasks, particularly through novel scanning strategies (e.g., fractal, atrous, windowed, and multi-scale scans) within model architectures like Mamba and its variants (e.g., ZeroMamba, GroupMamba). This approach offers a compelling alternative to computationally expensive transformers, with applications ranging from image classification and object detection to more specialized tasks like image deblurring, crowd counting, and remote sensing image analysis.