Structured Cross-Resolution Distillation for Remote Sensing Salient Object Detection

This is the authors' abstract. We don't add key points for this paper.

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: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lyf0801/SCRKD</uri>