Not Just Learning From Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing

Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics from the support images to guide the query image segmentation. However, such a pattern of "learning from others" struggles to handle the extreme intraclass variation, preventing FSS from being directly generalized to remote sensing scenes. To bridge the gap of intraclass variance, we develop a dual-mining network named DMNet for cross-image mining and self-mining, meaning that it no longer focuses solely on support images but pays more attention to the query image itself. Specifically, we propose a class-public region mining (CPRM) module to effectively suppress irrelevant feature pollution by capturing the common semantics between the support-query image pair. The class-specific region mining (CSRM) module is then proposed to continuously mine the class-specific semantics of the query image itself in a "filtering" and "purifying" manner. In addition, to prevent the coexistence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new known-class metasuppressor (KMS) module to suppress the activation of known-class objects in the sample. Extensive experiments on the iSAID and LoveDA remote sensing datasets have demonstrated that our method sets the state of the art with a minimum number of model parameters. Significantly, our model with the backbone of Resnet-50 achieves the mean Intersection over Union (mIoU) of 49.58% and 51.34% on iSAID under 1- and 5-shot settings, outperforming the state-of-the-art method by 1.8% and 1.12%, respectively. The code is publicly available at https://github.com/HanboBizl/DMNet/.