Hand pose estimation
US11775836B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 20, 2020 |
| Grant date | Oct 3, 2023 |
| Priority date | — |
| Expiry date | May 20, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/117
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A neural network in multi-task deep learning paradigm for machine vision includes an encoder that further includes a first, a second, and a third tier. The first tier comprises a first-tier unit having one or more first-unit blocks. The second tier receives a first-tier output from the first tier at one or more second-tier units in the second tier, a second-tier unit comprises one or more second-tier blocks, the third tier receives a second-tier output from the second tier at one or more third-tier units in the third tier, and a third-tier block comprises one or more third-tier blocks. The neural network further comprises a decoder operatively the encoder to receive an encoder output from the encoder as well as one or more loss function layers that are configured to backpropagate one or more losses for training at least the encoder of the neural network in a deep learning paradigm.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.