Transforming convolutional neural networks for visual sequence learning
US11049018B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 25, 2018 |
| Grant date | Jun 29, 2021 |
| Priority date | — |
| Expiry date | May 2, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/41
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method, computer readable medium, and system are disclosed for visual sequence learning using neural networks. The method includes the steps of replacing a non-recurrent layer within a trained convolutional neural network model with a recurrent layer to produce a visual sequence learning neural network model and transforming feedforward weights for the non-recurrent layer into input-to-hidden weights of the recurrent layer to produce a transformed recurrent layer. The method also includes the steps of setting hidden-to-hidden weights of the recurrent layer to initial values and processing video image data by the visual sequence learning neural network model to generate classification or regression output data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.