Name
Abstract | ||
|---|---|---|
Recently, deep convolutional neural networks have achieved significant success in salient object detection. However, existing state-of-the-art methods require high-end GPUs to achieve real-time performance, which makes it hard to adapt to low cost or portable devices. Although generic network architectures have been proposed to speed up inference on mobile devices, they are tailored to the task of... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCYB.2020.2969282 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Convolution,Object detection,Correlation,Neural networks,Image segmentation,Inference algorithms,Instruction sets | Journal | 51 |
Issue | ISSN | Citations |
12 | 2168-2267 | 2 |
PageRank | References | Authors |
0.35 | 26 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Feng Li | 1 | 8 | 3.15 |
| Guanbin Li | 2 | 259 | 37.61 |
| Binbin Yang | 3 | 2 | 0.35 |
| Guanqi Chen | 4 | 3 | 1.04 |
| Liang Lin | 5 | 3007 | 151.07 |
| Yizhou Yu | 6 | 2907 | 181.26 |