Name
Title | ||
|---|---|---|
FC-RCCN: Fully convolutional residual continuous CRF network for semantic segmentation |
Abstract | ||
|---|---|---|
•A semantic segmentation framework composed of three subnetworks is proposed.•The framework relies on multi-scale features fusion based network architecture.•The pairwise network is effective in learning the affinity matrixes.•The C-CRF network is very useful in refining the segmentation masks.•The C-CRF based learning strategy can boost the segmentation performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patrec.2018.08.030 | Pattern Recognition Letters |
Keywords | Field | DocType |
Continuous conditional random field (C-CRF),Semantic segmentation,Unary network,Pairwise network | Cross entropy,Unary operation,Pattern recognition,Softmax function,Segmentation,Network architecture,Robustness (computer science),Supervised learning,Artificial intelligence,Optimization problem,Mathematics | Journal |
Volume | ISSN | Citations |
130 | 0167-8655 | 1 |
PageRank | References | Authors |
0.35 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Zhou | 1 | 3 | 2.39 |
| Xiangyong Kong | 2 | 1 | 0.35 |
| Chen Gong | 3 | 401 | 44.73 |
| Fan Zhang | 4 | 229 | 69.82 |
| Xiaoguo Zhang | 5 | 1 | 0.35 |