Cross-Domain Few-Shot Learning Between Different Imaging Modals for Fine-Grained Target Recognition

Fine-grained target recognition in synthetic aperture radar (SAR) or infrared imaging modal is an open problem in some application scenarios where training samples are scarce. Transferring common features from visible optical (VO) samples is effective for the case that SAR (infrared) samples are scarce. However, for the fine-grained target recognition, transferring common features face two issues: first, common features can be divided into fine-grained features and the coarse-grained features. For the fine-grained target recognition task in the few-shot case, how to transfer fine-grained common features needed to be considered. Second, in the SAR (infrared) imaging modal, parts of samples carry much noise because of the limitation of the imaging mechanism, masking the subtle difference for the fine-grained target recognition task, making such fine-grained common features not easy to be transferred, especially when training samples are scarce. To handle these issues, corresponding solutions are proposed in this article as follows: first, the common-feature-contrastive loss is proposed to transfer fine-grained common features from VO samples; second, based on the modeling of the heteroscedastic uncertainty, the training strategy of sample quality evaluation is proposed to emphasize the training samples with less noise. Experiments on three datasets, including MSTAR, P-openSARship, and P-VAIS, represent the superiority of the proposed algorithm over baseline and other popular cross-domain few-shot learning algorithms.