Bidirectional feature learning network for RGB-D salient object detection

RGB-D salient object detection aims to perform the pixel-wise localization of salient objects from both RGB and depth images, whose challenge mainly comes from how to learn complementary features from each modality. Existing works often use increasingly large models for performance enhancement, which need large memory and time consumption in practice. In this paper, we propose a simple yet effective B idirectional F eature L earning Net work (BFLNet) for RGB-D salient object detection under limited memory and time conditions. To achieve accurate performance with lightweight backbone networks , an effective B idirectional F eature F usion (BFF) module is designed to merge features from both RGB and depth streams, in which the cross-modal fusions and cross-scale fusions are jointly conducted to fuse the immediate features in multiple scales and multiple modals. What is more, a simple D ual C onsistency L oss (DCL) function is designed to prompt cross-modal fusion by keeping the consistency between cross-modal target predictions. Extensive experiments on four benchmark datasets demonstrate that our method has achieved the state-of-the-art performance with high efficiency in RGB-D salient object detection. Code will be available at https://github.com/nightsky-nostar/BFLNet . • We design a bidirectional feature fusion model to learn discriminative features. • We propose a dual consistency loss to learn complementary information. • We achieve the state-of-the-art performance on several datasets.