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Grant Award View - GA28770-V3
Towards data-efficient future action prediction in the wild
GA ID:
GA28770-V3
Agency:
Australian Research Council
Approval Date:
27-Nov-2018
Variation Publish Date:
14-Oct-2021
Variation Date:
12-Oct-2021
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-May-2019 to 30-Apr-2022
Value (AUD):
$393,000.00
(GST inclusive where applicable)
Varies:
GA28770
- Towards data-efficient future action prediction in the wild
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:
Monash University
Recipient ABN:
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