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 intra-class variation, preventing FSS from being directly generalized to remote sensing scenes. To bridge the gap of intra-class 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 co-existence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new Known-class Meta Suppressor (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 mIoU of 49.58% and 51.34% on iSAID under 1-shot 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.

翻译：小样本分割（Few-shot Segmentation, FSS）旨在仅利用少量标注样本对未知类别的目标进行分割。当前大多数FSS方法遵循从支持图像中挖掘语义以引导查询图像分割的范式。然而，这种"向他人学习"的模式难以应对极端的类内差异，阻碍了FSS直接泛化至遥感场景。为弥合类内差异，我们提出了一种名为DMNet的双重挖掘网络，用于跨图像挖掘和自挖掘，这意味着它不再仅关注支持图像，而是更加注重查询图像本身。具体而言，我们提出类公共区域挖掘（Class-public Region Mining, CPRM）模块，通过捕捉支持-查询图像对之间的共有语义，有效抑制无关特征污染。随后提出类特定区域挖掘（Class-specific Region Mining, CSRM）模块，以"过滤"和"净化"的方式持续挖掘查询图像自身的类特定语义。此外，为防止遥感场景中多类别共存加剧FSS泛化崩溃，我们还提出一种新的已知类元抑制器（Known-class Meta Suppressor, KMS）模块，以抑制样本中已知类物体的激活。在iSAID和LoveDA遥感数据集上的大量实验表明，我们的方法以最少的模型参数实现了最优性能。值得注意的是，采用Resnet-50骨干网络的模型在iSAID数据集上，1-shot和5-shot设置下分别达到49.58%和51.34%的mIoU，领先现有最优方法1.8%和1.12%。代码已开源至https://github.com/HanboBizl/DMNet。