SF-Mamba: Rethinking State Space Model for Vision
SF-Mamba introduces two innovations for vision models: auxiliary patch swapping and batch folding with periodic state reset. Swapping allows bidirectional information flow across layers, while batch folding optimizes GPU usage for short sequences. These methods result in a novel Mamba architecture that achieves superior accuracy and throughput trade-offs compared to existing state-of-the-art models.