Name
Abstract | ||
|---|---|---|
Context is known to be one of crucial factors effecting the performance improvement of semantic segmentation. However, state-of-the-art segmentation models built upon fully convolutional networks are inherently weak in encoding contextual information because of stacked local operations such as convolution and pooling. Failing to capture context leads to inferior segmentation performance. Despite m... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPR48806.2021.9413174 | 2020 25th International Conference on Pattern Recognition (ICPR) |
Keywords | DocType | ISSN |
Radio frequency,Convolution,Semantics,Benchmark testing,Encoding,Pattern recognition,Kernel | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-7281-8808-9 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dehui Li | 1 | 0 | 0.34 |
| Zhiguo Cao | 2 | 314 | 44.17 |
| Ke Xian | 3 | 55 | 8.99 |
| Xinyuan Qi | 4 | 0 | 0.68 |
| Chao Zhang | 5 | 0 | 0.68 |
| Hao Lu | 6 | 140 | 20.86 |