Method and system for semantic segmentation involving multi-task convolutional neural network
US10467500B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 31, 2018 |
| Grant date | Nov 5, 2019 |
| Priority date | — |
| Expiry date | Dec 31, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems involving convolutional neural networks as applicable for semantic segmentation, including multi-task convolutional networks employing curriculum based transfer learning, are disclosed herein. In one example embodiment, a method of semantic segmentation involving a convolutional neural network includes training and applying the convolutional neural network. The training of the convolutional neural network includes each of training a semantic segmentation decoder network of the convolutional neural network, generating first feature maps by way of an encoder network of the convolutional neural network, based at least in part upon a dataset received at the encoder network, and training an instance segmentation decoder network of the convolutional neural network based at least in part upon the first feature maps. The applying includes receiving an image, and generating each of a semantic segmentation map and an instance segmentation map in response to the receiving of the image, in a single feedforward pass.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.