Name
Abstract | ||
|---|---|---|
In this paper, we address RGB-D salient object detection task by jointly leveraging semantics and contour details of salient objects. We propose a novel semantics-and-details complementary fusion network to adaptively integrate cross-model and multilevel features. Specifically, we employ two kinds of fusion modules in our model, which are designed for fusing high-level semantic features and integrating contour detail features of the scene components, respectively. The semantics fusion module aggregates high-level interdependent semantic relationships by a nonlinear weighted summation of small and medium receptive fields. Meanwhile, the details module integrates multi-level contour detail features to leverage expressive details of salient objects. We achieve new state-of-the-art salient object detection results on seven RGB-D datasets, that is, STERE, NJU2000, LFSD, NLPR, SSD, DES, and SIP2019 dataset. Experimental results demonstrate that our method outperforms eleven state-of-the-art salient object detection methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/cav.1954 | COMPUTER ANIMATION AND VIRTUAL WORLDS |
Keywords | DocType | Volume |
cross-model and multilevel features, feature fusion and deep fusion, RGB-D, salient object detection | Journal | 31 |
Issue | ISSN | Citations |
4-5 | 1546-4261 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shimin Zhao | 1 | 0 | 0.34 |
| Miaomiao Chen | 2 | 0 | 0.34 |
| Peng Jie Wang | 3 | 3 | 1.79 |
| Ying Cao | 4 | 87 | 9.28 |
| Pingping Zhang | 5 | 317 | 20.08 |
| Xin Yang | 6 | 200 | 36.16 |