Scene-specific Multi-Prototype Network for Remote Sensing Scene Graph Generation

Remote sensing scene graph generation aims to capture both objects and their semantic relationships, offering a comprehensive understanding of complex scenes. However, two major challenges hinder the performance of existing methods. First, remote sensing images often contain a large number of objects, many of which are unrelated. Performing global feature interactions across all objects introduces noise from irrelevant pairs, degrading feature quality. Second, relationship categories in remote sensing scenes exhibit significant intra-class variation across different contexts, and long-tailed distribution further complicates learning due to limited samples for tail classes. To address these issues, we propose the Scene-specific Multi-Prototype Network (SSMP). Our method performs contextual interactions selectively based on object and relationship categories, reducing interference from irrelevant features. Moreover, we introduce a scene-specific multi-prototype classification framework that better captures the diverse visual manifestations of each relationship class, while also improving discrimination under long-tailed distributions. Experimental results demonstrate that the proposed model achieves state-of-the-art (SOTA) performance, with a minimum improvement of 4.5% and a maximum improvement of 21.3% in mR@20 on the PredCls task over baseline models.