Scene graph generation for unlabeled data
US11995883B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 2, 2023 |
| Grant date | May 28, 2024 |
| Priority date | — |
| Expiry date | Feb 2, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/56
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.