LAUSR

The performance of recent RGB-D salient object detectors has significantly improved owing to their integration of convolutional neural networks (CNNs). However, most existing salient object detection (SOD) methods represent features using a VGGNet backbone, which lacks the ability to retain complete RGB and depth modals and must compensate by applying several skip connections. In this letter, we propose a gate fusion network (GFNet) with Res2Net architecture to solve this problem. GFNet consists of two interacting Res2Net block encoder streams and four gate fusion block (GFB) decoders to interconnect the streams and fuse features. Res2Net blocks have a robust feature retention mechanism to ensure that the decoders can learn complete information, while the GFB formulates the interdependences of the encoders and eliminates noise via a gate mechanism. We evaluated GFNet using two popular RGB-D salient detection benchmark datasets (NJU2000 and NLPR) and achieved state-of-the art performance.