MSNet: Multiple Strategy Network With Bidirectional Fusion for Detecting Salient Objects in RGB-D Images

Abstract

Various salient object detection (SOD) approaches have been developed to identify visually attractive objects in scenes captured in RGB-D (RGB and depth) images. High-level features often provide abstract semantics, and low-level features include more details such as textures and spatial structures. Hence, effectively fusing multimodal information from different levels has become a major area of development. We propose a multiple-strategy network (MSNet) with bidirectional fusion for RGB-D SOD that incorporates multilevel feature fusion and cross-modal aggregation into a multisupervised framework. We first use a multiple-strategy fusion module to transmit high-level semantic features along a top-down progressive pathway to generate a series of appearance features. Thereafter, a self-refinement module further refines and optimizes the saliency map. Furthermore, a depth optimization module strengthens depth information extraction, especially from low-quality depth maps. Extensive experimental results on seven benchmark datasets reveal the superiority and efficacy of the proposed MSNet, compared with state-of-the-art RGB-D SOD approaches.Note to Practitioners—This study introduces a RGB-D SOD network known as Multiple-Strategy Network (MSNet) with bidirectional fusion. Initially, we employ a multiple-strategy fusion module to transmit high-level semantic features in a top-down progressive manner, generating a sequence of appearance features. Subsequently, we apply a self-refinement module to further enhance and optimize the saliency map. Additionally, we incorporate a depth optimization module to improve the extraction of depth information, particularly from low-quality depth maps.

Topics & Concepts