  Recently, weakly supervised person search is proposed to discard
human-annotated identities and train the model with only bounding box
annotations. A natural way to solve this problem is to separate it into
detection and unsupervised re-identification (Re-ID) steps. However, in this
way, two important clues in unconstrained scene images are ignored. On the one
hand, existing unsupervised Re-ID models only leverage cropped images from
scene images but ignore its rich context information. On the other hand, there
are numerous unpaired persons in real-world scene images. Directly dealing with
them as independent identities leads to the long-tail effect, while completely
discarding them can result in serious information loss. In light of these
challenges, we introduce a Context-Guided and Unpaired-Assisted (CGUA) weakly
supervised person search framework. Specifically, we propose a novel
Context-Guided Cluster (CGC) algorithm to leverage context information in the
clustering process and an Unpaired-Assisted Memory (UAM) unit to distinguish
unpaired and paired persons by pushing them away. Extensive experiments
demonstrate that the proposed approach can surpass the state-of-the-art weakly
supervised methods by a large margin (more than 5% mAP on CUHK-SYSU). Moreover,
our method achieves comparable or better performance to the state-of-the-art
supervised methods by leveraging more diverse unlabeled data. Codes and models
will be released soon.
