System and method for efficiently amalgamated cnn-transformer architecture for mobile vision applications
US12373672B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 9, 2022 |
| Grant date | Jul 29, 2025 |
| Priority date | — |
| Expiry date | Apr 5, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/07
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An edge computing system, computer readable storage medium and method for object detection, including processing circuitry. The processing circuitry is configured with a hybrid CNN and vision transformer backbone network in an object detection deep learning network. The backbone network receives an image, and includes a first convolutional encoder to extract local features from feature maps of the image, a second stage having consecutive second convolutional encoders, a positional encoding layer, split depth-wise transpose attention (SDTA) encoders, consecutive convolutional encoders, a third stage and a fourth stage SDTA encoder. Each of the SDTA encoders perform multi-headed self-attention by applying a dot product operation across channel dimensions in order to compute cross-covariance across channels to generate attention feature maps. The object detection neural network includes a convolutional network that produces a fixed-size collection of bounding boxes and scores for a presence of object class instances in those boxes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.