ORSIFlow: Saliency-Guided Rectified Flow for Optical Remote Sensing Salient Object Detection

arXiv cs.CV / 4/27/2026

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Key Points

ORSIFlow is proposed as a saliency-guided rectified-flow framework to address the challenges of optical remote sensing salient object detection, such as complex backgrounds and low contrast.
The method reformulates ORSI-SOD as a deterministic latent flow generation problem, avoiding the stochastic sampling drawbacks and high computational cost of diffusion-based approaches.
ORSIFlow generates saliency masks in a compact latent space using a frozen variational autoencoder, enabling efficient inference with only a few steps.
It improves saliency quality via two components: a Salient Feature Discriminator for global semantic discrimination and a Salient Feature Calibrator for boundary refinement.
Experiments across multiple public benchmarks report state-of-the-art performance with substantially better efficiency than prior methods.

Abstract

Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) remains challenging due to complex backgrounds, low contrast, irregular object shapes, and large variations in object scale. Existing discriminative methods directly regress saliency maps, while recent diffusion-based generative approaches suffer from stochastic sampling and high computational cost. In this paper, we propose ORSIFlow, a saliency-guided rectified flow framework that reformulates ORSI-SOD as a deterministic latent flow generation problem. ORSIFlow performs saliency mask generation in a compact latent space constructed by a frozen variational autoencoder, enabling efficient inference with only a few steps. To enhance saliency awareness, we design a Salient Feature Discriminator for global semantic discrimination and a Salient Feature Calibrator for precise boundary refinement. Extensive experiments on multiple public benchmarks show that ORSIFlow achieves state-of-the-art performance with significantly improved efficiency.