TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small
  Target Detection
Tianxiang Chen Zhentao Tan Qi Chu Yue Wu Bin Liu Nenghai Yu
Infrared small target detection (ISTD) is critical to national security and
has been extensively applied in military areas. ISTD aims to segment small
target pixels from background. Most ISTD networks focus on designing feature
extraction blocks or feature fusion modules, but rarely describe the ISTD
process from the feature map evolution perspective. In the ISTD process, the
network attention gradually shifts towards target areas. We abstract this
process as the directional movement of feature map pixels to target areas
through convolution, pooling and interactions with surrounding pixels, which
can be analogous to the movement of thermal particles constrained by
surrounding variables and particles. In light of this analogy, we propose
Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical
principles of thermal conduction. According to thermal conduction differential
equation in heat dynamics, we derive the pixel movement differential equation
(PMDE) in the image domain and further develop two modules: Thermal
Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module
(TCBM). TCIA incorporates finite difference method with PMDE to reach a
numerical approximation so that target body features can be extracted. To
further remove errors in boundary areas, TCBM is designed and supervised by
boundary masks to refine target body features with fine boundary details.
Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our
method.