TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection
<|reference_start|>TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection: 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.<|reference_end|>
@article{chen2024tci-former:,
    title={TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small
  Target Detection},
    author={Tianxiang Chen, Zhentao Tan, Qi Chu, Yue Wu, Bin Liu, Nenghai Yu},
    journal={arXiv preprint arXiv:2402.02046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02046},
    primaryClass={cs.CV}
}