AUSLearn: AUtomated Sample Learning for Object Recognition

AUSLearn：用于对象识别的自动样本学习

• 批准号: FT210100228
• 负责人: Prof Wanli Ouyang
• 金额: --
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-06-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT210100228
• 关键词: AUSLearn; AUtomated; Sample; Learning; Object

This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.

该项目旨在使计算机能够学习如何有效地使用训练样本进行对象识别。训练样本是计算机学习识别物体的唯一来源。该项目创建了一个新的研究方向，将首次全面探索样本的力量。这些目标将通过利用强化学习、快速训练算法的最新进展以及开发新的深度学习算法来实现。新算法将有利于广泛的应用，例如有效地使用车祸训练样本来准确识别运输中潜在的道路碰撞，并有效地使用罕见的医学成像训练数据来稳健地诊断健康疾病。