Deep Learning Architecture with Context Adaptive Features for Image Parsing

用于图像解析的具有上下文自适应特征的深度学习架构

• 批准号: DP200102252
• 负责人: Prof Brijesh Verma
• 金额: $ 33.7万
• 依托单位: Griffith University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-01-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200102252
• 关键词: Deep; Learning; Architecture; Context; Adaptive

This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.

该项目旨在开发一种新型的深度学习网络架构，具有用于图像解析的上下文自适应功能，可以提高现实世界应用中的对象检测准确性。将开发和研究一些用于深度学习、上下文特征和网络参数选择的创新方法。所提出的架构和功能的影响将提高对象检测的准确性，并在图像解析的深度学习网络架构方面取得进展。预期成果是深度学习网络架构，上下文特征提取技术和图像解析的网络参数优化技术。