DMGNet: Depth mask guiding network for RGB-D salient object detection

Abstract

Though depth images can provide supplementary spatial structural cues for salient object detection (SOD) task, inappropriate utilization of depth features may introduce noisy or misleading features, which may greatly destroy SOD performance. To address this issue, we propose a depth mask guiding network (DMGNet) for RGB-D SOD. In this network, a depth mask guidance module (DMGM) is designed to pre-segment the salient objects from depth images and then create masks using pre-segmented objects to guide the RGB subnetwork to extract more discriminative features. Furthermore, a feature fusion pyramid module (FFPM) is employed to acquire more informative fused features using multi-branch convolutional channels with varying receptive fields, further enhancing the fusion of cross-modal features. Extensive experiments on nine benchmark datasets demonstrate the effectiveness of the proposed network.