DGFNet: Depth-Guided Cross-Modality Fusion Network for RGB-D Salient Object Detection

Abstract

RGB-D salient object detection (SOD) focuses on utilizing the complementary cues of RGB and depth modalities to detect and segment salient regions. However, many proposed methods train their models in a simple multi-modal manner, ignoring the differences between these two modalities in the contribution of salient detection. Furthermore, the quality of depth datasets varies significantly between individuals and is another important factor affecting model performance. To address the aforementioned issues, this article proposes a novel depth-guided fusion network framework (DGFNet) for the RGB-D SOD task. To avoid the influence of low-quality depth maps on RGB-D SOD, we design a depth map enhanced algorithm which jointly models salient detection and depth estimation to improve the quality of depth. Also, we propose a depth attention mechanism to encode valuable spatial information for SOD, which is then used in depth-guided fusion (DGF) module to guide the fusion of cross-modality features at each level. Extensive experiments on seven commonly tested datasets demonstrate that our DGFNet outperforms the 23 state-of-the-art RGB-D-based SOD methods.