Tracklet association methods learn the cross camera retrieval ability though associating underlying cross camera positive samples, which have proven to be successful in unsupervised person re-identification task. However, most of them use poor-efficiency association strategies which costs long training hours but gains the low performance. To solve this, we propose an effective end-to-end exemplar associations (EEA) framework in this work. EEA mainly adapts three strategies to improve efficiency: (1) end-to-end exemplar-based training, (2) exemplar association and (3) dynamic selection threshold. The first one is to accelerate the training process, while the others aim to improve the tracklet association precision. Compared with existing tracklet associating methods, EEA obviously reduces the training cost and achieves the higher performance. Extensive experiments and ablation studies on seven RE-ID datasets demonstrate the superiority of the proposed EEA over most state-of-the-art unsupervised and domain adaptation RE-ID methods.

小波关联方法通过关联基础交叉摄像机正样本来学习交叉摄像机检索能力，这已证明在无人监督的人员重新识别任务中是成功的。但是，他们中的大多数使用效率低下的关联策略，这会花费较长的培训时间，但性能却很差。为了解决这个问题，我们在这项工作中提出了一个有效的端到端范例协会（EEA）框架。EEA主要采用三种策略来提高效率：（1）端到端基于示例的训练；（2）示例关联和（3）动态选择阈值。第一个是要加快训练过程，而其他一个目的是提高小波关联精度。与现有的轨迹关联方法相比，EEA明显降低了训练成本，并获得了更高的性能。在七个RE-ID数据集上进行的大量实验和消融研究表明，所提出的EEA优于大多数最新的无监督和领域自适应RE-ID方法。