Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we propose a Support Pair Active Learning (SPAL) framework to lower the manual labeling cost for large-scale person reidentification. The support pairs can provide the most informative relationships and support the discriminative feature learning. Specifically, we firstly design a dual uncertainty selection strategy to iteratively discover support pairs and require human annotations. Afterwards, we introduce a constrained clustering algorithm to propagate the relationships of labeled support pairs to other unlabeled samples. Moreover, a hybrid learning strategy consisting of an unsupervised contrastive loss and a supervised support pair loss is proposed to learn the discriminative re-id feature representation. The proposed overall framework can effectively lower the labeling cost by mining and leveraging the critical support pairs. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art active learning methods on large-scale person re-id benchmarks.

翻译：在这项工作中,我们提议了一个支持性对称学习框架,以降低大规模个人再定位的人工标签成本。支持性对口可以提供最丰富的信息关系并支持歧视性特征学习。具体地说,我们首先设计了双重的不确定性选择战略,以迭接发现支持配对并需要人的说明。随后,我们引入了有限的组合算法,以传播标签式支持配对与其他未加标签样本的关系。此外,我们提议采用混合学习战略,包括一种未经监督的对比性损失和受监督的支持性对口损失,以学习歧视性的重新定位特征代表。拟议的总体框架可以通过采矿和利用关键支持配对有效地降低标签成本。广泛的实验表明拟议方法优于关于大规模个人再定位基准的州级积极学习方法。