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Grant Award View - GA64761-V4

Automatic Machine Learning with Imperfect Data for Video Analysis

Contact Details

ARC NCGP General Enquiries

:
02 6287 6600

:

GA ID:
GA64761-V4
Agency:
Australian Research Council
Approval Date:
4-Dec-2019
Variation Publish Date:
4-Jul-2023
Variation Date:
20-Jun-2023
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-May-2020 to 31-Mar-2024
Value (AUD):
$486,000.00 (GST inclusive where applicable)
Varies:
GA64761 - Automatic Machine Learning with Imperfect Data for Video Analysis

One-off/Ad hoc:
No
Aggregate Grant Award:
No

PBS Program Name:
ARC 19/20 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Automatic Machine Learning with Imperfect Data for Video Analysis
Purpose:
This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be constructed. All of these will construct a stable and efficient deep neural network, which is able to process large size videos captured from real scenarios in high efficiencies. Various fields, such as health care service and cybersecurity, will benefit hugely from this project.

GO ID:
GO Title:
Discovery Projects for funding commencing in 2020
Internal Reference ID:
DP20 Round 1
Selection Process:
Targeted or Restricted Competitive

Confidentiality - Contract:
No
Confidentiality - Outputs:
No

Grant Recipient Details

Recipient Name:
University of Technology Sydney
Recipient ABN:
77 257 686 961

Grant Recipient Location

Suburb:
ULTIMO
Town/City:
ULTIMO
Postcode:
2007
State/Territory:
NSW
Country:
AUSTRALIA

Grant Delivery Location

State/Territory:
NSW
Postcode:
2007
Country:
AUSTRALIA

Contact Details

ARC NCGP General Enquiries

:
02 6287 6600

: