Extracting attributes from arbitrary digital images utilizing a multi-attribute contrastive classification neural network
US12136250B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 27, 2021 |
| Grant date | Nov 5, 2024 |
| Priority date | — |
| Expiry date | Jan 27, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/70
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.