Depth-Induced Gap-Reducing Network for RGB-D Salient Object Detection: An Interaction, Guidance and Refinement Approach

Depth provides complementary information for salient object detection (SOD). However, the performance of RGB-D SOD methods is usually hindered by low quality depth map, semantic gap cross-modality and intrinsic gap between multi-level features. Although recent RGB-D SOD methods have been embedded into depth quality assessment, these methods do not consider the inconsistency of the depth format across datasets. In this paper, we propose an interpretable and effective mechanism called interference degree (ID) to assess depth quality and reweight the contribution of single-modality features without extra annotation. Then, a cross-modality interaction block (CMIB) is designed to reduce the semantic gap between RGB and depth features with the help of ID mechanism, and a mutually guided cross-level fusion (MGCF) module is designed to reduce the intrinsic gap among multi-level features. Finally, a refinement branch is proposed to enhance the salient regions and suppress the non-salient regions of fused features. Extensive experiments on six benchmark datasets show that the proposed depth-induced gap-reducing network (DIGR-Net) outperforms 20 recent state-of-the-art methods.

Automated summary

In this paper, a deep-induced gap-reducing network (DIGR-Net) is proposed for assessing depth quality and enhancing salient object detection. The network utilizes an interpretable mechanism called interference degree (ID) to reweight feature contributions, and incorporates a cross-modality interaction block and mutually guided cross-level fusion module to reduce semantic and intrinsic gaps. A refinement branch is also introduced to enhance salient regions.