Self-Supporting Adaptive Prototype Learning for Remote Sensing Few-Shot Semantic Segmentation

The segmentation of remote sensing images with few shots is valuable both in theory and application. Many existing few-shot segmentation methods rely on prototype learning, where a single support prototype guides predictions for the query set. However, due to visual differences between the support and query sets, a single support prototype struggles to capture all semantic information effectively. This paper introduces an adaptive self-supporting prototype learning network to address these challenges. We propose adaptive hyper prototype representation (HPR), comprising hyper prototype clustering (HPC) and guided prototype matching (GPM). HPC, a parameter-free and adaptive method, extracts more representative prototypes by clustering similar feature vectors using superpixel features. GPM selects matched prototypes to offer more accurate guidance, ensuring a uniform representation of multiple prototypes and complex semantic information. We also present self-supporting matching (SSM) prototype learning, guiding query set segmentation by obtaining query set prototypes. SSM generates initial pseudo-labels for the query set based on support set prototypes and further guides the query set using its own features, avoiding visual differences between support and query sets. Our adaptive self-supporting prototype learning network significantly enhances prototype quality and outperforms on object-level remote sensing datasets.