Name
Abstract | ||
|---|---|---|
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empirically evaluate their impact on the final model performance through ablation study. By taking all the refinements, we improve the space-time memory networks to achieve a Overall of 79.1 on the Youtube-VOS Challenge 2019. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCVW.2019.00086 | 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) |
Keywords | Field | DocType |
Video Object Segmentation,Deep Learning,Convolutional Neural Network | Computer vision,Pattern recognition,Computer science,Segmentation,Artificial intelligence | Conference |
Volume | Issue | ISSN |
2019 | 1 | 2473-9936 |
ISBN | Citations | PageRank |
978-1-7281-5024-6 | 0 | 0.34 |
References | Authors | |
5 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongdong Yu | 1 | 63 | 7.07 |
| Kai Su | 2 | 8 | 1.79 |
| Hengkai Guo | 3 | 53 | 4.69 |
| Jian Wang | 4 | 7 | 6.40 |
| Kaihui Zhou | 5 | 0 | 1.35 |
| Yuanyuan Huang | 6 | 0 | 0.34 |
| Minghui Dong | 7 | 201 | 33.61 |
| Jie Shao | 8 | 69 | 11.99 |
| Changhu Wang | 9 | 1296 | 70.36 |