Mamba, an architecture with RNN-like token mixer of state space model (SSM),
was recently introduced to address the quadratic complexity of the attention
mechanism and subsequently applied to vision tasks. Nevertheless, the
performance of Mamba for vision is often underwhelming when compared with
convolutional and attention-based models. In this paper, we delve into the
essence of Mamba, and conceptually conclude that Mamba is ideally suited for
tasks with long-sequence and autoregressive characteristics. For vision tasks,
as image classification does not align with either characteristic, we
hypothesize that Mamba is not necessary for this task; Detection and
segmentation tasks are also not autoregressive, yet they adhere to the
long-sequence characteristic, so we believe it is still worthwhile to explore
Mamba's potential for these tasks. To empirically verify our hypotheses, we
construct a series of models named \emph{MambaOut} through stacking Mamba
blocks while removing their core token mixer, SSM. Experimental results
strongly support our hypotheses. Specifically, our MambaOut model surpasses all
visual Mamba models on ImageNet image classification, indicating that Mamba is
indeed unnecessary for this task. As for detection and segmentation, MambaOut
cannot match the performance of state-of-the-art visual Mamba models,
demonstrating the potential of Mamba for long-sequence visual tasks. The code
is available at this https URL

Mamba 适用于具有长序列和自回归特性的任务，但不适用于图像分类；Mamba 在目标检测和分割任务中表现不如注意力模型，但显示出在长序列视觉任务中的潜力。