We present PlainMamba: a simple non-hierarchical state space model (SSM)
designed for general visual recognition. The recent Mamba model has shown how
SSMs can be highly competitive with other architectures on sequential data and
initial attempts have been made to apply it to images. In this paper, we
further adapt the selective scanning process of Mamba to the visual domain,
enhancing its ability to learn features from two-dimensional images by (i) a
continuous 2D scanning process that improves spatial continuity by ensuring
adjacency of tokens in the scanning sequence, and (ii) direction-aware updating
which enables the model to discern the spatial relations of tokens by encoding
directional information. Our architecture is designed to be easy to use and
easy to scale, formed by stacking identical PlainMamba blocks, resulting in a
model with constant width throughout all layers. The architecture is further
simplified by removing the need for special tokens. We evaluate PlainMamba on a
variety of visual recognition tasks including image classification, semantic
segmentation, object detection, and instance segmentation. Our method achieves
performance gains over previous non-hierarchical models and is competitive with
hierarchical alternatives. For tasks requiring high-resolution inputs, in
particular, PlainMamba requires much less computing while maintaining high
performance. Code and models are available at
this https URL

PlainMamba 是一种简单的非层次化状态空间模型 (SSM)，用于通用视觉识别，通过选择性扫描过程将 Mamba 模型应用于视觉领域，提高其从二维图像中学习特征的能力，并通过在扫描序列中保证标记相邻性的连续 2D 扫描过程以及具有方向性的更新来区分标记的空间关系。我们的架构易于使用和扩展，通过堆叠相同的 PlainMamba 块形成宽度恒定的模型，同时去除了特殊标记的需求。我们在包括图像分类、语义分割、目标检测和实例分割在内的多种视觉识别任务上评估了 PlainMamba。与以前的非层次化模型相比，我们的方法取得了性能提升，并且与层次化模型相媲美。尤其对于需要高分辨率输入的任务，PlainMamba 在保持高性能的同时需要更少的计算资源。