#State Space Model

The Mamba project provides an innovative state space model architecture designed for efficient handling of information-dense data such as language models, overcoming the shortcomings of earlier subquadratic models. Its architecture, focusing on hardware efficiency similar to FlashAttention, utilizes selective state space modeling for scalable solutions. Pretrained models are available on Hugging Face, and the `lm-evaluation-harness` library enables evaluations. Comprehensive resources include installation guides, usage instructions, and benchmarking scripts to support seamless integration and performance optimization.

The RS-Mamba project features an innovative Recurrent State Space Model designed for dense prediction in large remote sensing images. By utilizing a state space model for the first time in this context, RS-Mamba achieves an effective global receptive field with linear complexity, setting new standards in semantic segmentation and change detection. This model is structured to optimally map spatial features across various directions, ensuring efficiency and power even with straightforward training methods. Explore the code and documentation to enhance remote sensing projects.

This resource provides a comprehensive list of papers dedicated to State-Space Models (SSMs) and their applications, emphasizing their potential as alternatives to Transformers in innovative network solutions. It encompasses detailed theoretical insights and practical applications, including visual object tracking, 3D pose estimation, and medical image processing. The collection boasts thorough surveys, theses references, and the latest research publications, serving as a valuable tool for academics and practitioners looking to keep pace with the evolving role of state-space modeling. It is regularly updated to reflect the latest progressions in the field.

The repository provides an extensive overview of methodologies in low-level vision tasks such as image restoration, super-resolution, deraining, and dehazing. It includes noteworthy models like MambaIR, RSDehamba, and MambaCSR, recognized by conferences including ECCV, ACM MM, and NeurIPS 2024. This collection is a valuable asset for those seeking to improve image quality using state-of-the-art state-space models.

Explore advanced visual representation learning through bidirectional state space models, achieving significant efficiency gains compared to traditional vision transformers such as DeiT. This research on Mamba models showcases a novel architecture for scalable vision backbones, optimizing computational and memory usage, thereby setting a new standard in visual data processing.