Dynamic selective network for RGB-D salient object detection

Hongfa WEN, Chenggang YAN, Xiaofei ZHOU*, Runmin CONG, Yaoqi SUN, Bolun ZHENG, Jiyong ZHANG*, Yongjun BAO, Guiguang DING

*Corresponding author for this work

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

48 Citations (Scopus)


RGB-D saliency detection is receiving more and more attention in recent years. There are many efforts have been devoted to this area, where most of them try to integrate the multi-modal information, i.e. RGB images and depth maps, via various fusion strategies. However, some of them ignore the inherent difference between the two modalities, which leads to the performance degradation when handling some challenging scenes. Therefore, in this paper, we propose a novel RGB-D saliency model, namely Dynamic Selective Network (DSNet), to perform salient object detection (SOD) in RGB-D images by taking full advantage of the complementarity between the two modalities. Specifically, we first deploy a cross-modal global context module (CGCM) to acquire the high-level semantic information, which can be used to roughly locate salient objects. Then, we design a dynamic selective module (DSM) to dynamically mine the cross-modal complementary information between RGB images and depth maps, and to further optimize the multi-level and multi-scale information by executing the gated and pooling based selection, respectively. Moreover, we conduct the boundary refinement to obtain high-quality saliency maps with clear boundary details. Extensive experiments on eight public RGB-D datasets show that the proposed DSNet achieves a competitive and excellent performance against the current 17 state-of-the-art RGB-D SOD models.

Original languageEnglish
Pages (from-to)9179-9192
Number of pages14
JournalIEEE Transactions on Image Processing
Early online date5 Nov 2021
Publication statusPublished - 2021
Externally publishedYes

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© 2021 IEEE.


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