JALNet: joint attention learning network for RGB-D salient object detection

The existing RGB-D saliency object detection (SOD) methods mostly explore the complementary information between depth features and RGB features. However, these methods ignore the bi-directional complementarity between RGB and depth features. From this view, we propose a joint attention learning network (JALNet) to learn the cross-modal mutual complementary effect between the RGB images and depth maps. Specifically, two joint attention learning networks are designed, namely, a cross-modal joint attention fusion module (JAFM) and a joint attention enhance module (JAEM), respectively. The JAFM learns cross-modal complementary information from the RGB and depth features, which can strengthen the interaction of information and complementarity of useful information. At the same time, we utilise the JAEM to enlarge receptive field information to highlight salient objects. We conducted comprehensive experiments on four public datasets, which proved that the performance of our proposed JALNet outperforms 16 state-of-the-art (SOTA) RGB-D SOD methods.