CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
School of Engineering, Westlake University, Hangzhou, China.
Neural Netw. 2020 Sep;129:43-54. doi: 10.1016/j.neunet.2020.05.015. Epub 2020 May 23.
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.
轨迹关联方法通过关联潜在的跨摄像机正样本来学习跨摄像机检索能力，这已被证明在无监督的人员重新识别任务中是成功的。然而，它们中的大多数使用效率低下的关联策略，这些策略需要很长的训练时间，但性能却很低。为了解决这个问题，我们在这项工作中提出了一种有效的端到端范例关联(EEA)框架。EEA 主要采用三种策略来提高效率：(1)端到端基于范例的训练，(2)范例关联和(3)动态选择阈值。第一个是加速训练过程，而其他的则旨在提高轨迹关联的精度。与现有的轨迹关联方法相比，EEA 明显降低了训练成本，并且实现了更高的性能。在七个 RE-ID 数据集上的广泛实验和消融研究表明，所提出的 EEA 优于大多数最先进的无监督和域自适应 RE-ID 方法。
