Towards Transferable Visual Understanding in the Real World

本项目旨在探讨如何提高视觉理解算法和系统在实际应用中的可移植性。该项目旨在创新和推进视觉迁移学习和广义视觉表征学习领域的知识。该项目的预期结果包括技术和算法，使视觉理解系统对不同的现实世界场景具有鲁棒性。该项目将提供显著的效益，如提高交通领域自动驾驶汽车的鲁棒性和安全性，以及降低先进制造领域智能故障检测的破坏性数据收集成本。

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.