Short Sequence Representation Learning with Limited Supervision

有限监督下的短序列表示学习

• 批准号: DP230101753
• 负责人: Prof Xue Li
• 金额: $ 28.52万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230101753
• 关键词: Short; Sequence; Representation; Learning; Limited

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 multimodality-integrated video technologies for cybersecurity and public safety.

基于短文本和视频数据的事件预测广泛应用于在线犯罪检测，网络攻击识别和公共安全保护等现实世界的应用中。然而，由于监管有限、多源异构、数据缺失和低质量等问题，开发这样一个有效的预测模型非常困难。本项目旨在应对这些挑战。该项目的预期成果将为有效的短序列表示学习奠定理论基础，并构建下一代智能系统。这将通过多模态集成视频技术在网络安全和公共安全方面的应用，使我们的社会和经济受益。