Coupled multi-task fully convolutional networks using multi-scale contextual information and hierarchical hyper-features for semantic image segmentation
US11538164B2 · kind B2 · utility
0Cited by
10References
20Claims
0Family size
Assignee
Inventors
Key dates
| Filing date | Dec 16, 2020 |
| Grant date | Dec 27, 2022 |
| Priority date | — |
| Expiry date | Jan 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques related to implementing fully convolutional networks for semantic image segmentation are discussed. Such techniques may include combining feature maps from multiple stages of a multi-stage fully convolutional network to generate a hyper-feature corresponding to an input image, up-sampling the hyper-feature and summing it with a feature map of a previous stage to provide a final set of features, and classifying the final set of features to provide semantic image segmentation of the input image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.