Parallax-Estimation-Enhanced Network With Interweave Consistency Feature Fusion for Binocular Salient Object Detection

Yun Zhu, Wujie Zhou, Qiang Li, Lu Yu

Abstract: Salient object detection (SOD) has received extensive attention in recent years, and many models have been developed. However, most SOD models only consider monocular images and not binocular images, which resemble the human vision and can better reflect human perception for distinguishing salient objects. To leverage the information in binocular images, we propose herein a first-of-its-kind parallax-estimation-enhanced network (PEENet) for binocular SOD. More specifically, we use a weighted binocular fusion module and a parallax correlation fusion module to explore the complementary and different information in binocular images. In addition, a parallax enhancing module and interweave consistency fusion use complementary saliency information and parallax information to enhance saliency and parallax representations. Finally, a transformation module avoids global and local information loss during decoding. Experiments were performed to validate the effectiveness and robustness of the proposed PEENet, which outperforms 10-RGB/RGB-D SOD methods on two binocular SOD datasets.