Identifying digital attributes from multiple attribute groups within target digital images utilizing a deep cognitive attribution neural network
US11386144B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 9, 2019 |
| Grant date | Jul 12, 2022 |
| Priority date | — |
| Expiry date | Jul 2, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating tags for an object portrayed in a digital image based on predicted attributes of the object. For example, the disclosed systems can utilize interleaved neural network layers of alternating inception layers and dilated convolution layers to generate a localization feature vector. Based on the localization feature vector, the disclosed systems can generate attribute localization feature embeddings, for example, using some pooling layer such as a global average pooling layer. The disclosed systems can then apply the attribute localization feature embeddings to corresponding attribute group classifiers to generate tags based on predicted attributes. In particular, attribute group classifiers can predict attributes as associated with a query image (e.g., based on a scoring comparison with other potential attributes of an attribute group). Based on the generated tags, the disclosed systems can respond to tag queries and search queries.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.