Invention Grant
- Patent Title: Dense and discriminative neural network architectures for improved object detection and instance segmentation
-
Application No.: US16845343Application Date: 2020-04-10
-
Publication No.: US11244188B2Publication Date: 2022-02-08
- Inventor: Hisham Cholakkal , Jiale Cao , Rao Muhammad Anwer , Fahad Shahbaz Khan , Yanwei Pang , Ling Shao
- Applicant: Inception Institute of Artificial Intelligence, Ltd.
- Applicant Address: AE Abu Dhabi
- Assignee: Inception Institute of Artificial Intelligence, Ltd.
- Current Assignee: Inception Institute of Artificial Intelligence, Ltd.
- Current Assignee Address: AE Abu Dhabi
- Agency: Bryan Cave Leighton Paisner LLP
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06K9/32 ; G06N3/08 ; G06K9/00 ; G06N3/04

Abstract:
This disclosure relates to improved techniques for performing computer vision functions, including common object detection and instance segmentation. The techniques described herein utilize neural network architectures to perform these functions in various types of images, such as natural images, UAV images, satellite images, and other images. The neural network architecture can include a dense location regression network that performs object localization and segmentation functions, at least in part, by generating offset information for multiple sub-regions of candidate object proposals, and utilizing this dense offset information to derive final predictions for locations of target objects. The neural network architecture also can include a discriminative region-of-interest (RoI) pooling network that performs classification of the localized objects, at least in part, by sampling various sub-regions of candidate proposals and performing adaptive weighting to obtain discriminative features.
Public/Granted literature
Information query