做海事监控或环境敏感异常检测的团队，RGFiLM能直接降低稀有场景下的误报率，值得在AIS数据上试试。
该研究提出Rarity-Gated Feature-wise Linear Modulation (RGFiLM)模块，用于解决上下文异常检测中稀有上下文分布导致的误报问题。RGFiLM通过数据驱动的稀有度评分控制上下文调制强度，在稀有上下文中增强决策能力，在常见上下文中保持保守。在海事轨迹异常检测任务中，结合AIS运动序列和ERA5环境上下文，RGFiLM在F1与假阳性率权衡上优于现有方法。结果表明，显式考虑上下文稀有性可有效减少异常检测中的误报。
Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection
Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where rare regimes can be critical information. Under such frequency bias, context-conditioned models can produce unstable decisions and excessive false alarms in rare contexts. We propose Rarity-Gated Feature-wise Linear Modulation (RGFiLM), a rarity-aware conditioning module that combines feature-wise modulation (i.e., context-conditioned scaling and shifting of hidden features) with a gate controlled by a data-driven rarity score. The rarity score is estimated from the empirical distribution of context variables and regulates how strongly context modulates intermediate representations: the gate becomes more decisive under rare contexts while remaining conservative under frequent contexts. We evaluate RGFiLM on maritime trajectory anomaly detection using AIS motion sequences with ERA5 environmental context in an environment-sensitive detour scenario. When instantiated in a sequential anomaly scoring pipeline, RGFiLM achieves the best mean F1--False Positive Rate (FPR) trade-off among the compared context-agnostic and context-conditioned methods. These results suggest that explicitly accounting for context rarity is an effective approach for reducing false alarms in context-sensitive anomaly detection.