Structured Cross-Resolution Distillation for Remote Sensing Salient Object Detection

Existing salient object detection methods for optical remote sensing images achieve superior results with high-resolution inputs but exhibit significant degradation in low-resolution conditions. To bridge this resolution discrepancy, we propose a structured cross-resolution knowledge distillation (SCRKD) framework designed for severely low-resolution inputs. It leverages high-resolution models as teachers to guide low-resolution students through three synergistic distillation mechanisms: 1) multiview correlation distillation (MVCD); 2) multiscale feature distillation (MSFD); and 3) decoupled saliency distillation (DSD). In addition, we present cascaded SCRKD that progressively refines structured knowledge in a multistage manner, achieving further performance boosts. Experiments on three datasets indicate that SCRKD surpasses 13 state-of-the-art methods across various cross-resolution settings. Besides, our framework based on three distinct baselines validates its model-agnostic nature. This work provides an efficient solution for low-resolution salient object detection. Code is available at: https://github.com/lyf0801/SCRKD