Systems and methods for semantic segmentation
US11461644B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 13, 2019 |
| Grant date | Oct 4, 2022 |
| Priority date | — |
| Expiry date | Mar 26, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Fully-supervised semantic segmentation machine learning models are augmented by ancillary machine learning models which generate high-detail predictions from low-detail, weakly-supervised data. The combined model can be trained over both fully- and weakly-supervised data. Only the primary model is required for inference, post-training. The combined model can be made self-correcting during training by adjusting the ancillary model's output based on parameters learned over both the fully- and weakly-supervised data. The self-correction module may combine the output of the primary and ancillary models in various ways, including through linear combinations and via neural networks. The self-correction module and ancillary model may benefit from disclosed pre-training techniques.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.