Systems and methods for improving visual search using summarization feature
US11017261B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 28, 2021 |
| Grant date | May 25, 2021 |
| Priority date | — |
| Expiry date | Jan 28, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/764
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for training a metric learning convolutional neural network (CNN)-based model for cross-domain image retrieval are disclosed. The methods and systems perform steps of generating a plurality of batches sampled from a cross-domain training dataset to train the CNN-based model to match images of different sub-categories from one domain to another, and training the CNN-based model using the generated batches. The CNN-based model comprises various pooling, normalization, and concatenation layers that enable it to concatenate the normalized outputs of multiple concatenation layers. Use of the generated batches comprises executing a loss function based on one or more batches, where the loss function is a triplet, contrastive, or cluster loss function. Embodiments of the present invention enable the CNN-based model to summarize information from multiple convolutional layers, thus improving visual search. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.