Lightweight Multi-Frequency Enhancement Network for RGB-D Video Salient Object Detection

Abstract

RGB-D Video Salient Object Detection has gained increasing interest, but existing models often struggle to balance efficiency and accuracy, hindering their applications on resource-constrained devices. A key challenge in designing lightweight models is maintaining accuracy while reducing parameters. To address this issue and bridge the gap in lightweight RGB-D VSOD research, we propose a lightweight network architecture using MobileNetV2 as the backbone. We introduce an Improved Cross-Shift Module (ICSM) to extract the fused depth and flow features with minimal overhead and a Multi-Frequency Enhancement Module (MFEM) to separate high-and low-frequency information and enhance the resulting feature maps using different techniques for final saliency prediction. Experimental results demonstrate that our method achieves competitive accuracy compared to non-efficient models, running at 80 FPS on a GPU with only 4.75M parameters, making it suitable for real-time applications. Code will be available at https://github.com/Tibetsonam/MFENet.