Dynamic Message Propagation Network for RGB-D and Video Salient Object Detection

Baian CHEN, Zhilei CHEN, Xiaowei HU, Jun XU, Haoran XIE*, Jing QIN, Mingqiang WEI*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


Exploiting long-range semantic contexts and geometric information is crucial to infer salient objects from RGB and depth features. However, existing methods mainly focus on excavating local features within fixed regions by continuously feeding forward networks. In this paper, we introduce Dynamic Message Propagation (DMP) to dynamically learn context information within more flexible regions. We integrate DMP into a Siamese-based network to process the RGB image and depth map separately, and design a multi-level feature fusion module to explore cross-level information between refined RGB and depth features. Extensive experiments show clear improvements of our method over seventeen methods on six benchmark datasets for RGB-D salient object detection (SOD). Additionally, our method outperforms its competitors for the video SOD task. Code is available at: https://github.com/chenbaian-cs/DMPNet.
Original languageEnglish
Number of pages21
JournalACM Transactions on Multimedia Computing, Communications and Applications
Publication statusE-pub ahead of print - 19 May 2023

Bibliographical note

This work was supported by the Shenzhen Science and Technology Program (No.JCYJ20220818103401003, No. JCYJ20220530172403007), by the General Program of Natural Science Foundation of Guangdong Province (No. 2022A1515010170), by the Free Exploration of Basic Research Project, Local Science and Technology Development Fund Guided by the Central Government of China (No.2021Szvup060), and the Research Grant entitled "Self Supervised Learning for Medical Images" (No.871228) and Shenzhen University-Lingnan University Joint Research Programme (SZU-LU006/2122) of Lingnan University, Hong Kong.


  • RGB-D salient object detection
  • dynamic message propagation
  • cross-modal learning
  • depth feature propagation


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