Dual-Stream Feature Map Reconstruction Network for Few-Shot Visible-Infrared Ship Recognition

Abstract

Few-shot learning enables rapid recognition of novel categories with limited samples, mimicking human cognition. However, existing unimodal few-shot image recognition methods often fail to maintain robustness under complex maritime disturbances. To address these challenges, the dual-stream feature map reconstruction network (DFRN) is proposed to address few-shot visible-infrared ship recognition. In this model, visible and infrared features are projected into a shared subspace for common semantics and into extended subspaces to extract modality-specific details. These parallel streams are then leveraged for collaborative reconstruction and fusion. Additionally, a full prototype constraint module (FPCM) is introduced to enhance intra-modality feature separability. A bidirectional probability collaborative learning module (BPCLM) is employed to facilitate mutual knowledge transfer across modalities. Furthermore, YTShip-10K is constructed: a large-scale ship dataset comprising 10083 paired visible and infrared images covering 101 fine-grained categories across five maritime scenarios. Experiments show that our model achieves 81.20% and 95.36% accuracy on 1-shot and 5-shot tasks, outperforming unimodal baselines by 4.94% and 5.29%, respectively. The YTShip-10K dataset and code will be publicly released.

Keywords

few-shot learning / image classification / image reconstruction / meta-learning / visible-infrared image