Automatic Training Data Search and Model Evaluation by Measuring Domain Gap

我们的目标是研究计算机视觉训练数据和测试数据，使用自动生成的数据集进行面部表情识别和对象重新识别。该项目希望量化和了解领域差距，即训练和测试数据集之间的分布差异。这个项目的预期结果是对测量领域差距的洞察，在不访问昂贵的测试标签的情况下估计模型性能的能力，以及对系统通用性的改进。这将为目前需要昂贵标签的计算机视觉应用提供显著好处，并在交通、安全和制造等行业带来商业和经济效益。

We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.