Abstract
Rotation equivariance constitutes one of the most general and crucial structural priors for visual data, yet it remains notably absent from current Mamba-based vision architectures. Despite the success of Mamba in natural language processing and its growing adoption in computer vision, existing visual Mamba models fail to account for rotational symmetry in their design. This omission renders them inherently sensitive to image rotations, thereby constraining their robustness and cross-task generalization. To address this limitation, we propose to incorporate rotation symmetry, a universal and fundamental geometric prior in images, into Mamba-based architectures. Specifically, we introduce EQ-VMamba, the first rotation equivariant visual Mamba architecture for vision tasks. The core components of EQ-VMamba include a carefully designed rotation equivariant cross-scan strategy and group Mamba blocks. Moreover, we provide a rigorous theoretical analysis of the intrinsic equivariance error, demonstrating that the proposed architecture enforces end-to-end rotation equivariance throughout the network. Extensive experiments across multiple benchmarks - including high-level image classification task, mid-level semantic segmentation task, and low-level image super-resolution task - demonstrate that EQ-VMamba achieves superior or competitive performance compared to non-equivariant baselines, while requiring approximately 50% fewer parameters. These results indicate that embedding rotation equivariance not only effectively bolsters the robustness of visual Mamba models against rotation transformations, but also enhances overall performance with significantly improved parameter efficiency. Code is available at this https URL.
Abstract (translated)
旋转等变性构成了视觉数据中最通用和最关键的结构先验之一，然而它在当前基于Mamba的视觉架构中却显著缺席。尽管Mamba在自然语言处理领域取得了成功，并且逐渐被应用于计算机视觉，现有的视觉Mamba模型在其设计中未能考虑旋转对称性。这种遗漏使它们内在地容易受到图像旋转的影响，从而限制了其鲁棒性和跨任务泛化能力。 为了解决这一局限性，我们提议将旋转对称性——一种普遍而基本的几何先验，在基于Mamba的架构中融入其中。具体而言，我们提出了EQ-VMamba，这是首个针对视觉任务的旋转等变视觉Mamba架构。EQ-VMamba的核心组件包括精心设计的旋转等变交叉扫描策略和群组Mamba块。此外，我们还提供了关于内在等变误差的严格理论分析，表明所提出的架构在整个网络中执行端到端的旋转等变性。 在多个基准测试——包括高层次图像分类任务、中层次语义分割任务和低层次图像超分辨率任务上的广泛实验显示，EQ-VMamba相较于非等变基线模型，不仅实现了优越或具有竞争力的表现，还减少了大约50%的参数需求。这些结果表明，在视觉Mamba模型中嵌入旋转等变性不仅能有效增强其对旋转变换的鲁棒性，还能通过显著提高参数效率来提升整体性能。 代码可在此链接获取：[提供URL的地方]（请确保替换为实际可用的有效链接）。
URL
https://arxiv.org/abs/2603.09138
