The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or the target classes. Previous models struggle to learn feature representations that generalize to various unknown domains from limited training domain samples. In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability. In this work, we propose a SAM-aware graph prompt reasoning network (GPRN) that fully leverages SAM to guide CD-FSS feature representation learning and improve prediction accuracy. Specifically, we propose a SAM-aware prompt initialization module (SPI) to transform the masks generated by SAM into visual prompts enriched with high-level semantic information. Since SAM tends to divide an object into many sub-regions, this may lead to visual prompts representing the same semantic object having inconsistent or fragmented features. We further propose a graph prompt reasoning (GPR) module that constructs a graph among visual prompts to reason about their interrelationships and enable each visual prompt to aggregate information from similar prompts, thus achieving global semantic consistency. Subsequently, each visual prompt embeds its semantic information into the corresponding mask region to assist in feature representation learning. To refine the segmentation mask during testing, we also design a non-parameter adaptive point selection module (APS) to select representative point prompts from query predictions and feed them back to SAM to refine inaccurate segmentation results. Experiments on four standard CD-FSS datasets demonstrate that our method establishes new state-of-the-art results.

Few-shot semantic segmentation (FSS) aims to segment a query image using a limited number of densely annotated support images from the same category. Most existing conventional FSS methods are tailored for coping with images from natural scenarios. Unlike natural images, remote sensing images usually have a similar background context among the support and query image pairs, and more severe intraclass inconsistency exists due to overhead shooting views. However, facing such realistic and challenging remote sensing FSS tasks, the existing methods seldom consider these intrinsic characteristics from a unified viewpoint, thus leading to inferior results. To solve the above dilemma, we propose a multi-granularity aggregation network (MGANet) to progressively capture multi-granularity discriminative information, for tackling the remote sensing FSS task. Specifically, MGANet consists of a multi-granularity similarity (MGS) module and an adaptive multiprototype aggregation (AMPA) module. To fully utilize background context, MGS extracts multi-granularity support and query feature maps from the backbone network to calculate a holistic correlation by incorporating the background information. Next, to alleviate the intraclass inconsistency of remote sensing images, AMPA decomposes the support foreground region into mainstay and auxiliary subregions by the guidance of reverse prediction on support features, thus generating three types of prototypes by masked average pooling (MAP) on these paired features and masks. Furthermore, these multiprototypes are collaboratively interacted with the query features to pursue reinforced discriminative features, relying on prototype-aware slot attention (PASA). Extensive experiments on iSAID-$5^{i}$and LoveDA-$2^{i}$demonstrate well the superiority of the proposed MGANet. The source code is available athttps://github.com/CVL-hub/MGANet/.