Name
Title | ||
|---|---|---|
Effective full-scale detection for salient object based on condensing-and-filtering network |
Abstract | ||
|---|---|---|
•There are still two challenges in the field of salient object detection: 1) The lack of rich features extracted from multiple perspectives at different encoder levels results in the omission of salient objects with varying scales. 2) The ineffective fusion of multi-level features during decoding dilutes the saliency features, which destroys the purity of the predicted maps.•To solve the above two problems, we propose a Condensing-and-Filtering Network (CFNet), in which a saliency pyramid condensing module (SPCM) and a saliency filtering module (SFM) are proposed to achieve an effective full-scale detection for salient objects.•Experimental results demonstrate that the proposed method outperforms 23 state-of-the-art methods with a real-time speed and considerable computation on five benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108904 | Pattern Recognition |
Keywords | DocType | Volume |
Salient object detection,Neural networks,Full-scale feature extraction,Multi-level feature fusion | Journal | 131 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 34 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Yan | 1 | 0 | 0.34 |
| Meijun Sun | 2 | 74 | 11.77 |
| Ya-Hong Han | 3 | 476 | 44.97 |
| Zheng Wang | 4 | 72 | 47.08 |
| Qi Tian | 5 | 6443 | 331.75 |