Name
Title | ||
|---|---|---|
Multi-scale feature aggregation and boundary awareness network for salient object detection |
Abstract | ||
|---|---|---|
Salient object detection aims to detect the most visually distinctive objects in an image. Although existing FCN-based methods have shown strong advantages in this field, scale variation and complex boundary are still great challenges. In this paper, we propose a multi-scale feature aggregation and boundary awareness network to overcome the problems. Multi-scale feature aggregation module is proposed to integrate adjacent hierarchical features and the multiple aggregation strategy solves the problem of scale variation. To obtain more effective multi-scale features from integrated features, a cross feature refinement module is proposed to compose the decoder. For the issue of complex boundary, we design a boundary pixel awareness loss function to enable the network to acquire boundary information and generate high-quality saliency maps with better boundary. Experiments on five benchmark datasets show that our network outperforms recent state-of-the-art detectors quantitatively and qualitatively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.imavis.2022.104442 | Image and Vision Computing |
Keywords | DocType | Volume |
Deep learning,Salient object detection,Feature aggregation,Complex boundary | Journal | 122 |
ISSN | Citations | PageRank |
0262-8856 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Wu | 1 | 0 | 0.34 |
| Jianzhe Wang | 2 | 0 | 0.34 |
| Zhilei Chai | 3 | 0 | 0.34 |
| Guodong Guo | 4 | 2548 | 144.00 |