Towards Data-Efficient Future Action Prediction in the Wild

Project Member(s): Chang, X.

Funding or Partner Organisation: Australian Research Council (ARC DECRA Scheme)
Australian Research Council (ARC DECRA Scheme)

Start year: 2019

Summary: This project aims to build state-of-the-art deep learning models to predict future actions in videos with a handful of labeled examples. The project expects to produce the next great step for machine intelligence - the potential to explore a handful of labeled 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.

Publications:

Huang, Z, Lin, S, Liu, G, Luo, M, Ye, C, Xu, H, Chang, X & Liang, X 1970, 'FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, pp. 3479-3488.
View/Download from: Publisher's site

FOR Codes: Applied computing, Artificial intelligence, Data mining and knowledge discovery, Information retrieval and web search

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.