Learning model architecture for image data semantic segmentation
US11694301B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2020 |
| Grant date | Jul 4, 2023 |
| Priority date | — |
| Expiry date | Feb 4, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20221
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A learning model may provide a hierarchy of convolutional layers configured to perform convolutions upon image features, each layer other than a topmost layer convoluting the image features at a lower resolution to a higher layer, and each layer other than a bottommost layer returning the image features to a lower layer. Each layer fuses the lower resolution image features received from a higher layer with same resolution image features convoluted at the layer, so as to combine large-scale and small-scale features of images. Layers of the hierarchy may be substantially equal to a number of lateral convolutions at a bottommost convolutional layer. The bottommost convolutional layer ultimately passes the fused features to an attention mapping module, which utilizes two attention mapping pathways in combination to detect non-local dependencies and interactions between large-scale and small-scale features of images without de-emphasizing local interactions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.