OCShell-Mamba: Object-Centric Shell Propagation for Vision State Space Models

Vision state space models (SSMs) achieve efficient visual modeling by serializing 2D feature maps into 1D sequences. However, serialization is not representation-invariant: the scan order determines how information accumulates in the recurrent state. Conventional scan paths, such as raster and bidirectional scans, often disrupt local neighborhoods and fragment object structures, while existing adaptive scans remain token-centric and do not explicitly preserve object-level coherence. We propose OCShell-Mamba (Object-Centric Shell routing Mamba), an object-centric vision SSM that propagates states over object-like regions instead of individual tokens. Without requiring external supervision, OCShell-Mamba learns an objectness field and organizes state propagation using a shell routing strategy that expands from each region center toward its boundary. Its core $\underline{S}$hell-wise $\underline{O}$bject-$\underline{C}$entric r$\underline{O}$uting (SOCO) module partitions each region into concentric shells, aggregates features within each shell, and processes shell descriptors from inner to outer. This design better aligns sequence adjacency with image geometry while preserving local and object-level coherence. We further introduce $\underline{P}$ersistent t$\underline{o}$pology $\underline{Me}$mory (PoMe), a fixed-size memory bank that maintains object-level representations across resolution changes. On ImageNet-1K, ADE20K, and COCO, OCShell-Mamba consistently outperforms a matched DAMamba baseline, achieving 83.9% top-1 accuracy, 50.8% mIoU, 49.1% box AP, and 43.7% mask AP. These results show that vision SSMs benefit not only from adaptive scan paths but also from explicitly modeling object-centric state propagation.