CAMN-FSOD: Class-aware memory network for few-shot infrared object detection

Abstract

Cross-Domain Few-Shot Object Detection (CD-FSOD) from visible to infrared domains faces a critical challenge: object classification proves significantly more error-prone than localization under fine-tuning adaptation. This stems from substantial representational discrepancies in internal object features between domains, which hinder effective transfer. To enhance the saliency of infrared internal object features and mitigate classification errors in few-shot visible-to-infrared transfer, we propose the Class-Aware Memory Network for Few-Shot Object Detection (CAMN-FSOD). CAMN explicitly memories high-quality internal object features during fine-tuning and leverages memory to augment features,boosting recognition accuracy during inference. Furthermore, we introduce our two-stage Decoupled-Coupled Fine-tuning approach (DCFA) to combat CAMN overfitting in few-shot training and maximize its effectiveness. We establish a visible-infrared FSOD benchmark dataset for evaluation. Extensive experiments demonstrate that CAMN-FSOD significantly enhances the few-shot learning capability of the base model without increasing trainable parameters. In the 1-shot setting, our method achieves 42.0 mAP₅₀, which is 14.4 points higher than the baseline, and an overall mAP of 25.2, showing an improvement of 2.3 points, outperforming existing methods.