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Grant Award View - GA28770

Towards data-efficient future action prediction in the wild

Contact Details

ARC NCGP General Enquiries

:
02 6287 6600

:

GA ID:
GA28770
Agency:
Australian Research Council
Approval Date:
27-Nov-2018
Publish Date:
18-Dec-2018
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-May-2019 to 31-Dec-2023
Original: 1-Jan-2019 to 31-Dec-2021
Value (AUD):
$393,000.00 (GST inclusive where applicable)
Variations:

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

PBS Program Name:
ARC 18/19 Discovery
Grant Program:
Discovery Early Career Researcher Award
Grant Activity:
Towards data-efficient future action prediction in the wild
Purpose:
This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generation of intelligent systems to accommodate limited supervision. This should benefit science, society, and the economy nationally through the applications of autonomous vehicles, sensor technologies, and cybersecurity.

GO ID:
GO Title:
Discovery Early Career Researcher Award commencing in 2019
Internal Reference ID:
DE19 Round 1
Selection Process:
Targeted or Restricted Competitive

Confidentiality - Contract:
No
Confidentiality - Outputs:
No

Grant Recipient Details

Recipient Name:
University of Technology Sydney
Original: Monash University
Recipient ABN:
77 257 686 961
Original: 12 377 614 012

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

: