Title
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network
ORCID Identifiers
Document Type
Conference Proceeding
Source of Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication Date
1-1-2020
Abstract
© 2020, Springer Nature Switzerland AG. Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting and classifying objects at various scales. When multi-level features meet multi-modal cues, the optimal fusion problem becomes a hot potato. In this paper, we make the first attempt to leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to develop a novel cascaded refinement network. In particular, we 1) propose a bifurcated backbone strategy (BBS) to split the multi-level features into teacher and student features, and 2) utilize a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views. This fuses RGB and depth modalities in a complementary way. Our simple yet efficient architecture, dubbed Bifurcated Backbone Strategy Network (BBS-Net), is backbone independent and outperforms 18 SOTAs on seven challenging datasets using four metrics.
DOI Link
ISBN
9783030586096
ISSN
Publisher
Springer International Publishing
Volume
12357 LNCS
First Page
275
Last Page
292
Disciplines
Computer Sciences
Keywords
Bifurcated backbone strategy, RGB-D saliency detection
Recommended Citation
Fan, Deng Ping; Zhai, Yingjie; Borji, Ali; Yang, Jufeng; and Shao, Ling, "BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network" (2020). All Works. 651.
https://zuscholars.zu.ac.ae/works/651
Indexed in Scopus
no
Open Access
no