# M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection Source: [M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:34:16.637Z Authors: Xian Fang, Jinchao Zhu, Ruixun Zhang, Xiuli Shao, Hongpeng Wang Published: 2021-09-16T12:15:40.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()