Zero-shot and few-shot learning with deep knowledge transfer

具有深度知识迁移的零样本和少样本学习

• 批准号: DE170101259
• 负责人: Dr Lingqiao Liu
• 金额: $ 25.19万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE170101259
• 关键词: Zero; shot; few; learning; deep

This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/few-shot learning more practical, scalable and flexible. The project is expected to advance the applicability of visual recognition in many challenging scenarios and provide effective tools to analyse the online visual data for supporting Australia’s cybernetic security.

这个项目旨在开发少镜头和零镜头学习，这是一种视觉识别技术，可以在很少或没有视觉例子的情况下学习视觉概念。视觉识别是人工智能的重要组成部分，广泛应用于控制论安全、机器人视觉和医学图像分析等领域。该项目将使用深度学习来使零/少射击学习能够使用和模拟以前未探索的信息，使零/少射击学习更实用、可扩展和灵活。该项目预计将促进视觉识别在许多具有挑战性的场景中的适用性，并为支持澳大利亚的控制论安全提供有效的工具来分析在线视觉数据。