MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small-Target Detection

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, that is, 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 (SSM) for ISTD tasks in terms of effectiveness and efficiency in the article. However, directly applying Mamba achieves suboptimal performances due to the insufficient harnessing of local features, which…

• NN
  National Natural Science Foundation of China

  Awards: 62121002, 62272430
• NS
  National Science and Technology Major Project

  Award: 2023z020006
• FR
  Fundamental Research Funds for the Central Universities