Multi-task learning for real-time semantic and/or depth aware instance segmentation and/or three-dimensional object bounding
US10984290B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 31, 2019 |
| Grant date | Apr 20, 2021 |
| Priority date | — |
| Expiry date | Dec 31, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30261
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Training a machine-learning (ML) architecture to determine three or more outputs at a rate of 30 or more frames per second on consumer grade hardware may comprise jointly training components of the ML using loss(es) determined across the components and/or consistency losses determined between outputs of two or more components. The ML architecture discussed herein may comprise one or more sets of neural network layers and/or respective components for determining a two and/or three-dimensional region of interest, semantic segmentation, direction logits, depth data, and/or instance segmentation associated with an object in an image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.