Salient object detection is a fundamental topic in computer vision. Previous methods based on RGB-D often suffer from the incompatibility of multi-modal feature fusion and the insufficiency of multi-scale feature aggregation. To tackle these two dilemmas, we propose a novel multi-modal and multi-scale refined network (M2RNet). Three essential components are presented in this network. The nested dual attention module (NDAM) explicitly exploits the combined features of RGB and depth flows. The adjacent interactive aggregation module (AIAM) gradually integrates the neighbor features of high, middle and low levels. The joint hybrid optimization loss (JHOL) makes the predictions have a prominent outline. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches.
@article{arxiv.2109.07922,
title = {M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection},
author = {Xian Fang and Jinchao Zhu and Ruixun Zhang and Xiuli Shao and Hongpeng Wang},
journal= {arXiv preprint arXiv:2109.07922},
year = {2021}
}