As a challenging task, unsupervised person ReID aims to match the same identity with query images which does not require any labeled information. In general, most existing approaches focus on the visual cues only, leaving potentially valuable auxiliary metadata information (e.g., spatio-temporal context) unexplored. In the real world, such metadata is normally available alongside captured images, and thus plays an important role in separating several hard ReID matches. With this motivation in mind, we propose~\textbf{MGH}, a novel unsupervised person ReID approach that uses meta information to construct a hypergraph for feature learning and label refinement. In principle, the hypergraph is composed of camera-topology-aware hyperedges, which can model the heterogeneous data correlations across cameras. Taking advantage of label propagation on the hypergraph, the proposed approach is able to effectively refine the ReID results, such as correcting the wrong labels or smoothing the noisy labels. Given the refined results, We further present a memory-based listwise loss to directly optimize the average precision in an approximate manner. Extensive experiments on three benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.
翻译:作为具有挑战性的任务,无人监督的人ReID旨在将同一身份与不需要任何标签信息的查询图像相匹配。一般而言,大多数现有方法仅侧重于视觉线索,留下潜在有价值的辅助元数据信息(例如spatio-时间环境)未被探索。在现实世界中,这类元数据通常与所捕获的图像并存,因此在分离若干硬ReID匹配方面起着重要作用。本着这一动机,我们提议“textbf{MGH}”这一新的未经监督的人 ReID方法,它使用元信息构建一个用于特征学习和标签改进的超文本图。原则上,超文本由摄像-地形学-认知高端组成,可以建模各种相机之间的数据关联关系。利用高光谱上的标签宣传,拟议方法能够有效地改进ReID结果,例如纠正错误标签或平滑动噪音标签。根据改进的结果,我们进一步提出一个基于记忆的列表,以便直接以近似的方式优化平均精确度。关于三种基准的大规模实验展示了拟议状态方法的有效性。