论文标题
未知的人：一条自适应的管道，对无偿人员重新识别
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification
论文作者
论文摘要
人重新识别（REID）的主要困难在于收集带注释的数据并将模型转移到不同领域。本文介绍了《无情的人》，这是一种充分利用虚幻图像数据的新型管道，以降低培训和部署阶段的成本。它的基本部分是一个可以生成高质量和可控分布的合成图像的系统。实例级注释与综合数据有关，几乎是免费的。我们指出图像合成中的一些细节，在很大程度上影响了数据质量。有3,000个ID和120,000个实例，我们的方法直接转移到MSMT17时就可以达到38.5％的排名1精度。它几乎使用综合数据使以前的记录翻了一番，甚至超过了使用真实数据的先前直接传输记录。这为无监督的域适应提供了良好的基础，在该域中，我们的预训练模型很容易插入最新的算法中，以提高准确性。此外，可以灵活地调整数据分布以适合某些角落的REID场景，从而扩大了我们的管道的应用。我们将在https://github.com/flyhighest/unrealperson中发布数据综合工具包和合成数据。
The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part is a system that can generate synthesized images of high-quality and from controllable distributions. Instance-level annotation goes with the synthesized data and is almost free. We point out some details in image synthesis that largely impact the data quality. With 3,000 IDs and 120,000 instances, our method achieves a 38.5% rank-1 accuracy when being directly transferred to MSMT17. It almost doubles the former record using synthesized data and even surpasses previous direct transfer records using real data. This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. In addition, the data distribution can be flexibly adjusted to fit some corner ReID scenarios, which widens the application of our pipeline. We will publish our data synthesis toolkit and synthesized data in https://github.com/FlyHighest/UnrealPerson.
