论文标题
仔细研究合成：重新识别的人的细粒度分析
Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification
论文作者
论文摘要
人重新识别（RE-ID）在公共安全和视频监视等应用中起着重要作用。最近，从合成数据中受益的合成数据中学习的性能出色。但是，为了追求高准确性，学术界的研究人员始终以高度的时间和标签开支为重点，同时忽略了从数百万合成数据中进行有效培训的潜力。为了促进该领域的发展，我们回顾了先前开发的合成数据集GPR，并建立了具有更多身份和杰出属性的改进的数据集（GPR+）。基于它，我们定量分析数据集属性对重新ID系统的影响。据我们所知，我们是从合成数据集的属性方面明确剖析人员重新剖析的首次尝试。这项研究有助于我们更深入地了解Re-ID中的基本问题，这也为数据集构建和未来实际用途提供了有用的见解。
Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. However, in pursuit of high accuracy, researchers in the academic always focus on training with large-scale datasets at a high cost of time and label expenses, while neglect to explore the potential of performing efficient training from millions of synthetic data. To facilitate development in this field, we reviewed the previously developed synthetic dataset GPR and built an improved one (GPR+) with larger number of identities and distinguished attributes. Based on it, we quantitatively analyze the influence of dataset attribute on re-ID system. To our best knowledge, we are among the first attempts to explicitly dissect person re-ID from the aspect of attribute on synthetic dataset. This research helps us have a deeper understanding of the fundamental problems in person re-ID, which also provides useful insights for dataset building and future practical usage.
