Dual-View Classifier Evolution for Generalized Remote Sensing Few-Shot Segmentation

Advancements in few-shot segmentation (FSS) for remote sensing images have significantly improved the ability to binarization parse novel classes using only a few supports. Generalized few-shot segmentation (GFSS), a challenging and practical task, has recently attracted research attention. It involves recognizing base and novel classes while segmenting multiple categories in a query. Most GFSS methods adopt a two-stage approach: base classifier training and novel classifier registering. However, they encounter two key challenges: the data scale disparity between base and novel classes and significant intraclass variation in remote sensing images. In this article, we present a dual-view classifier evolution (DiCE) method. Our approach utilizes the well-trained base classifier to allocate attention within the novel classifier, effectively addressing the disparities between the two. Simultaneously, it fosters context-driven interactions between the query and the classifier, tailoring sample-specific classifiers to mitigate intraclass variations. Furthermore, we propose a binocular hybrid training (BHT) mechanism that integrates normal base training with episodic training, endowing the model with the ability to adapt to few-shot tasks. Extensive experiments on the iSAID-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{i}$ </tex-math></inline-formula> dataset demonstrate the superior performance of DiCE.