Towards Transferable Visual Understanding in the Real World

迈向现实世界中可迁移的视觉理解

• 批准号: DE220101379
• 负责人: Dr Guoliang Kang
• 金额: --
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE220101379
• 关键词: Towards; Transferable; Visual; Understanding; Real

This project aims to investigate how to improve the transferability of visual understanding algorithm and system in the real-world applications. This project expects to innovate and advance knowledge in the fields of visual transfer learning and generalizable visual representation learning. Expected outcomes of this project include techniques and algorithms to make the visual understanding system robust to diverse real-world scenarios. This project should provide significant benefits, such as improving the robustness and safety of autonomous vehicles in transportation area, and reducing the cost of destructive data collection for intelligent fault detection in advanced manufacturing area.

本计画旨在探讨如何提升视觉理解演算法与系统在实际应用中的可移植性。该项目期望在视觉迁移学习和可推广的视觉表征学习领域创新和推进知识。该项目的预期成果包括技术和算法，使视觉理解系统对不同的现实世界场景具有鲁棒性。该项目将带来显著的效益，例如提高交通领域自动驾驶汽车的鲁棒性和安全性，降低先进制造领域智能故障检测的破坏性数据收集成本。