  Recently, infrared small target detection (ISTD) has made significant
progress, thanks to the development of basic models. Specifically, the models
combining CNNs with transformers can successfully extract both local and global
features. However, the disadvantage of the transformer is also inherited, i.e.,
the quadratic computational complexity to sequence length. Inspired by the
recent basic model with linear complexity for long-distance modeling, Mamba, we
explore the potential of this state space model for ISTD task in terms of
effectiveness and efficiency in the paper. However, directly applying Mamba
achieves suboptimal performances due to the insufficient harnessing of local
features, which are imperative for detecting small targets. Instead, we tailor
a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient ISTD. It consists
of Outer and Inner Mamba blocks to adeptly capture both global and local
features. Specifically, we treat the local patches as "visual sentences" and
use the Outer Mamba to explore the global information. We then decompose each
visual sentence into sub-patches as "visual words" and use the Inner Mamba to
further explore the local information among words in the visual sentence with
negligible computational costs. By aggregating the visual word and visual
sentence features, our MiM-ISTD can effectively explore both global and local
information. Experiments on NUAA-SIRST and IRSTD-1k show the superior accuracy
and efficiency of our method. Specifically, MiM-ISTD is $8 \times$ faster than
the SOTA method and reduces GPU memory usage by 62.2$\%$ when testing on $2048
\times 2048$ images, overcoming the computation and memory constraints on
high-resolution infrared images.
