OCTOPUS：通过多维扫描和遍历选择增强视觉SSM的空间感知
OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selection
AI总结：
OCTOPUS通过多维扫描和遍历选择，提升视觉SSM的空间感知能力，实现高效的空间结构捕捉与全局上下文保留。
摘要：
状态空间模型（SSMs）最近作为一种替代Transformer的方案出现，因其在文本中建模全局关系的线性复杂度能力而备受关注。然而，其在视觉任务中的成功受到限制，因为其因果性公式适合于顺序文本，但在空间域中却不利，因为因果性破坏了像素或块之间的固有空间关系。因此，标准SSMs无法捕捉局部空间一致性，常常将非相邻块连接起来，而忽略那些视觉上相关的邻近块。为了解决这些限制，我们引入OCTOPUS，一种新的架构，在保持SSMs线性复杂度的同时，既保留了图像中的全局上下文，又保留了局部空间结构。OCTOPUS在八个主方向上进行离散重复，向前或向后在水平、垂直和对角方向上进行，允许在所有空间连接区域之间有效交换信息，同时保持无关块之间的独立性。这种设计使多向重复成为可能，利用SSM级别的效率捕捉全局上下文和局部空间结构。在我们的分类和分割基准测试中，OCTOPUS在边界保持和区域一致性方面表现出显著改进，这从分割结果中可以明显看出，同时在保持相对更好的分类准确率方面优于现有基于V-SSM的模型。这些结果表明，OCTOPUS似乎成为多向重复的基础方法，作为一种可扩展且有效的机制，用于构建空间感知且计算高效的视觉架构。
英文摘要：
State space models (SSMs) have recently emerged as an alternative to transformers due to their unique ability of modeling global relationships in text with linear complexity. However, their success in vision tasks has been limited due to their causal formulation, which is suitable for sequential text but detrimental in the spatial domain where causality breaks the inherent spatial relationships among pixels or patches. As a result, standard SSMs fail to capture local spatial coherence, often linking non-adjacent patches while ignoring neighboring ones that are visually correlated. To address these limitations, we introduce OCTOPUS , a novel architecture that preserves both global context and local spatial structure within images, while maintaining the linear complexity of SSMs. OCTOPUS performs discrete reoccurrence along eight principal orientations, going forward or backward in the horizontal, vertical, and diagonal directions, allowing effective information exchange across all spatially connected regions while maintaining independence among unrelated patches. This design enables multi-directional recurrence, capturing both global context and local spatial structure with SSM-level efficiency. In our classification and segmentation benchmarks, OCTOPUS demonstrates notable improvements in boundary preservation and region consistency, as evident from the segmentation results, while maintaining relatively better classification accuracy compared to existing V-SSM based models. These results suggest that OCTOPUS appears as a foundation method for multi-directional recurrence as a scalable and effective mechanism for building spatially aware and computationally efficient vision architectures.
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