Lightweight ORSI Salient Object Detection via Frequency and Mutual Assistance Attention

作者：Gongyang Li, Shixiang Shi, Yong Wu, W. N. Lin, Zhen Bai · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2026 · DOI：10.1109/tgrs.2026.3680324 · 被引用次数：2 · 研究领域：Visual Attention and Saliency Detection、Advanced Neural Network Applications、Face Recognition and Perception

Lightweight Salient Object Detection in Optical Remote Sensing Image (ORSI-SOD) is expected to achieve a good balance between model complexity and detection accuracy. Existing lightweight ORSI-SOD methods usually adopt the MobileNet as the backbone, which greatly reduces the model complexity, but also restricts the detection accuracy. In this paper, we propose a novel lightweightFrequency andMutualAssistance AttentionNetwork,i.e., FreMaNet, with a lightweight transformer backbone for ORSI-SOD. Our FreMaNet is built on the strategy of intra-level modeling and inter-level assistance. Frequency-domain Self-Attention (FreSA) and Mutual Assistance Channel Attention (MaCA) are responsible for intra-level modeling and inter-level assistance, respectively. Specifically, FreSA is arranged behind the backbone to further model global relationships within each level of features (i.e., intra-level features). Different from the vanilla self-attention, FreSA achieves global relationship modeling through multiplication in the frequency domain, resulting in less computational load. Then, different levels of features (i.e., inter-level features) assist and interact with each other in MaCA. MaCA first performs a simple fusion on the features of two adjacent levels, and then adopts parallel self-channel attention and assistance channel attention to adaptively achieve mutual assistance of features at different levels. With the cooperation of the above components and an efficient saliency decoder,...