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Short Sequence Representation Learning with Limited Supervision (2023-2026)

Abstract

Predicting events based on short text and video data is widely found in real-world applications such as online crime detection, cyber-attack identification, and public security protection. However, to develop such an effective prediction model is very difficult due to the problems such as limited supervision, heterogeneous multiple sources, and missing and low-quality data. This project is to tackle these challenges. Expected outcome of this project will lay a theoretical foundation for effective short sequence representation learning and build next-generation intelligent systems. This should benefit our society and economy through the applications of multimodalityintegrated video technologies for cybersecurity and public safety.

Experts

Professor Xue Li

Affiliate of ARC COE for Children and Families Over the Lifecourse
ARC Centre of Excellence: Children and Families Over the Lifecourse
Faculty of Humanities, Arts and Social Sciences
Professor
School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
Xue Li
Xue Li

Associate Professor Sen Wang

Associate Professor
School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
Sen Wang
Sen Wang