  Supervised learning is dominant in person search, but it requires elaborate
labeling of bounding boxes and identities. Large-scale labeled training data is
often difficult to collect, especially for person identities. A natural
question is whether a good person search model can be trained without the need
of identity supervision. In this paper, we present a weakly supervised setting
where only bounding box annotations are available. Based on this new setting,
we provide an effective baseline model termed Region Siamese Networks
(R-SiamNets). Towards learning useful representations for recognition in the
absence of identity labels, we supervise the R-SiamNet with instance-level
consistency loss and cluster-level contrastive loss. For instance-level
consistency learning, the R-SiamNet is constrained to extract consistent
features from each person region with or without out-of-region context. For
cluster-level contrastive learning, we enforce the aggregation of closest
instances and the separation of dissimilar ones in feature space. Extensive
experiments validate the utility of our weakly supervised method. Our model
achieves the rank-1 of 87.1% and mAP of 86.0% on CUHK-SYSU benchmark, which
surpasses several fully supervised methods, such as OIM and MGTS, by a clear
margin. More promising performance can be reached by incorporating extra
training data. We hope this work could encourage the future research in this
field.
