Visual SSMs
SSM: State Space Model
Overview
Visual state-space models adapt recurrent sequence architectures, especially selective state-space models such as Mamba, to images and video. They turn spatial or temporal features into sequences using directional scans, while newer designs explore bidirectional scans, graphs, clusters, frequency-domain processing, and combinations with CNNs or attention. These approaches are used for classification, segmentation, restoration, depth estimation, and video understanding, with benchmarks spanning natural images, medical scans, and remote sensing. A central tension is how to retain linear-time efficiency without losing local detail, boundary accuracy, or robustness to scan direction and domain shift.
Latest papers
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