  Similar to Vision Transformers, this paper identifies artifacts also present
within the feature maps of Vision Mamba. These artifacts, corresponding to
high-norm tokens emerging in low-information background areas of images, appear
much more severe in Vision Mamba -- they exist prevalently even with the
tiny-sized model and activate extensively across background regions. To
mitigate this issue, we follow the prior solution of introducing register
tokens into Vision Mamba. To better cope with Mamba blocks' uni-directional
inference paradigm, two key modifications are introduced: 1) evenly inserting
registers throughout the input token sequence, and 2) recycling registers for
final decision predictions. We term this new architecture Mamba-R. Qualitative
observations suggest, compared to vanilla Vision Mamba, Mamba-R's feature maps
appear cleaner and more focused on semantically meaningful regions.
Quantitatively, Mamba-R attains stronger performance and scales better. For
example, on the ImageNet benchmark, our base-size Mamba-R attains 82.9%
accuracy, significantly outperforming Vim-B's 81.8%; furthermore, we provide
the first successful scaling to the large model size (i.e., with 341M
parameters), attaining a competitive accuracy of 83.2% (84.5% if finetuned with
384x384 inputs). Additional validation on the downstream semantic segmentation
task also supports Mamba-R's efficacy.
