CHAL: Causal-guided Hierarchical Anomaly-aware Learning for Moving Infrared Small Target Detection

Infrared small target detection is one highly special category of object detection, faced with tiny target imaging size and cluttered backgrounds. Currently, almost all existing methods are target-centered, directly learning the target features from backgrounds. However, due to weak target signals, they are often difficult in effectively capturing stable features. Sometimes, they cannot even distinguish real targets from background confounders. To overcome these problems, from an opposite perspective, we propose the first Causal-guided Hierarchical Anomaly-aware Learning (CHAL) framework. Breaking through target-centered paradigm, it focuses on background learning, while the targets are handled as the anomalies in backgrounds. In detail, to fulfill the goal, a spatio-temporal neural field is designed to model the background evolution patterns from generative perspective. Meanwhile, a hierarchical anomaly-aware learning is proposed to decompose anomaly discovery. Furthermore, to block the spurious correlations often caused by background confounders, and enhance true target causality, a causal-guiding mechanism is designed. The experiments on three infrared datasets verify the effectiveness and superiority of our CHAL. Even in visible-light scenarios, it still possesses obvious adaptivity. Code is open at https://github.com/UESTC-nnLab/CHAL.