  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.

Computer Vision and Pattern Recognition