CMIGNet: Cross-Modal Inverse Guidance Network for RGB-Depth salient object detection

Abstract

Currently, the majority of RGB-Depth salient object detection (SOD) methods utilize the encoder–decoder architecture. However, they often fail to utilize the encoding and decoding features fully. This paper rethinks the differences and correlations between them and proposes the Cross-Modal Inverse Guidance Network (CMIGNet) for SOD. Specifically, a Multi-level Feature Guidance Enhancement (MFGE) module is integrated into every layer of the foundational network. It employs a high-level decoding feature to guide the low-level RGB and depth encoding features, facilitating the rapid identification of salient regions and noise removal. The dual-stream encoding features guided by the MFGE module are combined using the proposed Dual-Stream Interactive Fusion (DSIF) module. It could simultaneously reduce dependence on two modal features during the fusion process. Thus, the impact on the results can be reduced in complex scenes when one modality is absent or confusing. Finally, the edge information is supplemented using the proposed Edge Refinement Awareness (ERA) module to generate the final salient map. Comparisons on seven widely used and one latest challenging RGB-D datasets show that the performance of the proposed CMIGNet is highly competitive with the state-of-the-art RGB-Depth SOD models. Additionally, our model is lighter and faster.