Distilling Knowledge From Super-Resolution for Efficient Remote Sensing Salient Object Detection

Current state-of-the-art remote sensing salient object detectors always require high-resolution spatial context to ensure excellent performance, which incurs enormous computation costs and hinders real-time efficiency. In this work, we propose a universal super-resolution-assisted learning (SRAL) framework to boost performance and accelerate the inference efficiency of existing approaches. To this end, we propose to reduce the spatial resolution of the input remote sensing images (RSIs), which is model-agnostic and can be applied to existing algorithms without extra computation cost. Specifically, a transposed saliency detection decoder (TSDD) is designed to upsample interim features progressively. On top of it, an auxiliary SR decoder (ASRD) is proposed to build a multitask learning (MTL) framework to investigate an efficient complementary paradigm of saliency detection and SR. Furthermore, a novel task-fusion guidance module (TFGM) is proposed to effectively distill domain knowledge from the SR auxiliary task to the salient object detection task in optical RSIs. The presented ASRD and TFGM can be omitted in the inference phase without any extra computational budget. Extensive experiments on three datasets show that the presented SRAL with $224 \times 224$ input is superior to more than 20 algorithms. Moreover, it can be successfully generalized to existing typical networks with significant accuracy improvements in a parameter-free manner. Codes and models are available at https://github.com/lyf0801/SRAL.