Semantic-compensated Meta-learning for Few-shot SAR Target Detection

High-value novel-class SAR targets exhibit distributional differences from base-class SAR targets, leading existing FSOD methods to suffer from model bias and poor generalization. To address this issue and more effectively detect few-shot targets, this paper introduces a semantic compensation method based on meta-learning (SCML). Specifically, we generate support compensation features enriched with query semantic information to mitigate distribution discrepancies and guide the model in adapting to new-class SAR targets under few-shot conditions, thereby significantly improving detection performance. On existing public datasets, our method demonstrates superior detection accuracy compared to other SOTA approaches.