A central objective in computer vision is to design models with appropriate
2-D inductive bias. Desiderata for 2D inductive bias include two-dimensional
position awareness, dynamic spatial locality, and translation and permutation
invariance. To address these goals, we leverage an expressive variation of the
multidimensional State Space Model (SSM). Our approach introduces efficient
parameterization, accelerated computation, and a suitable normalization scheme.
Empirically, we observe that incorporating our layer at the beginning of each
transformer block of Vision Transformers (ViT) significantly enhances
performance for multiple ViT backbones and across datasets. The new layer is
effective even with a negligible amount of additional parameters and inference
time. Ablation studies and visualizations demonstrate that the layer has a
strong 2-D inductive bias. For example, vision transformers equipped with our
layer exhibit effective performance even without positional encoding

该研究旨在设计拥有适当 2D 归纳偏倚的计算机视觉模型。通过引入一种多维状态空间模型（SSM）的表现形式，这种方法有效地促进了 Vision Transformers（ViT）的性能。ViT 的新层引入了高效参数化，加速计算和合适的归一化方案，提供了强健的 2D 归纳偏倚特征，甚至即使在添加了轻微量的附加参数和推理时间的情况下仍然有效。