TCI-Former：热传导启发的红外小目标检测Transformer
TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection
AI总结：
本文提出TCI-Former，将红外小目标检测中的注意力转移类比为热传导，基于热传导微分方程推导像素运动微分方程，并设计TCIA和TCBM模块，在IRSTD-1k和NUAA-SIRST上取得优越性能。
摘要：
红外小目标检测（ISTD）对国家安全至关重要，并已广泛应用于军事领域。ISTD旨在从背景中分割出小目标像素。大多数ISTD网络专注于设计特征提取块或特征融合模块，但很少从特征图演化的角度描述ISTD过程。在ISTD过程中，网络注意力逐渐向目标区域转移。我们将这一过程抽象为特征图像素通过卷积、池化及与周围像素的交互向目标区域的方向性移动，这可以类比于受周围变量和粒子约束的热粒子的运动。基于这一类比，我们根据热传导的理论原理，提出了热传导启发的Transformer（TCI-Former）。根据热动力学中的热传导微分方程，我们推导出图像域中的像素运动微分方程（PMDE），并进一步开发了两个模块：热传导启发注意力（TCIA）和热传导边界模块（TCBM）。TCIA将有限差分法与PMDE相结合，以达到数值近似，从而提取目标主体特征。为了进一步消除边界区域的误差，设计了TCBM，并由边界掩码监督，以细化目标主体特征，同时保留精细的边界细节。在IRSTD-1k和NUAA-SIRST上的实验证明了我们方法的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
