Name
Abstract | ||
|---|---|---|
In this paper, we propose two novel components for improving deep salient object detection models. The first component, called saliency detection network (S-Net), introduces dense short- and long-range connections that effectively integrate multiscale features to better exploit contexts at multiple levels. Benefiting from the direct access to low- and high-level features, the S-Net can not only ex... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMM.2018.2830098 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Saliency detection,Object detection,Feature extraction,Image segmentation,Computational modeling,Deep learning,Neural networks | Distraction,Computer vision,Object detection,Task analysis,Pattern recognition,Salience (neuroscience),Computer science,Feature extraction,Exploit,Image segmentation,Artificial intelligence,Artificial neural network | Journal |
Volume | Issue | ISSN |
20 | 12 | 1520-9210 |
Citations | PageRank | References |
3 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaxin Xiao | 1 | 22 | 8.41 |
| Jiashi Feng | 2 | 2165 | 140.81 |
| Yunchao Wei | 3 | 769 | 47.16 |
| Maojun Zhang | 4 | 314 | 48.74 |
| Shuicheng Yan | 5 | 162 | 10.26 |