BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network
Source of Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
© 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.
Springer International Publishing
Bifurcated backbone strategy, RGB-D saliency detection
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.
Indexed in Scopus