Name
Abstract | ||
|---|---|---|
Recent work has achieved great success in utilizing global contextual information for semantic segmentation, including increasing the receptive field and aggregating pyramid feature representations. In this paper, we go beyond global context and explore the fine-grained representation using co-occurrent features by introducing Co-occurrent Feature Model, which predicts the distribution of co-occurrent features for a given target. To leverage the semantic context in the co-occurrent features, we build an Aggregated Co-occurrent Feature (ACF) Module by aggregating the probability of the co-occurrent feature with the co-occurrent context. ACF Module learns a fine-grained spatial invariant representation to capture co-occurrent context information across the scene. Our approach significantly improves the segmentation results using FCN and achieves superior performance 54.0% mIoU on Pascal Context, 87.2% mIoU on Pascal VOC 2012 and 44.89% mIoU on ADE20K datasets. The source code and complete system will be publicly available upon publication. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00064 | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Keywords | Field | DocType |
Deep Learning,Scene Analysis and Understanding,Segmentation,Grouping and Shape | Computer vision,Pattern recognition,Computer science,Segmentation,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1063-6919 | 978-1-7281-3294-5 | 13 |
PageRank | References | Authors |
0.49 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hang Zhang | 1 | 55 | 5.04 |
| Han Zhang | 2 | 243 | 15.29 |
| Chenguang Wang | 3 | 261 | 23.77 |
| Junyuan Xie | 4 | 429 | 13.62 |