Name
Title | ||
|---|---|---|
Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection. |
Abstract | ||
|---|---|---|
Compared to conventional saliency detection by handcrafted features, deep convolutional neural networks (CNNs) recently have been successfully applied to saliency detection field with superior performance on locating salient objects. However, due to repeated sub-sampling operations inside CNNs such as pooling and convolution, many CNN-based saliency models fail to maintain fine-grained spatial det... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMM.2018.2859746 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Image segmentation,Object detection,Feature extraction,Saliency detection,Convolution,Machine learning,Task analysis | Conditional random field,Computer vision,Object detection,Pattern recognition,Salience (neuroscience),Convolutional neural network,Segmentation,Computer science,Image segmentation,Feature extraction,Artificial intelligence,Deep learning | Journal |
Volume | Issue | ISSN |
21 | 2 | 1520-9210 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keren Fu | 1 | 295 | 26.25 |
| Qijun Zhao | 2 | 419 | 38.37 |
| Irene Yu-Hua Gu | 3 | 613 | 35.06 |