  In the conventional person re-id setting, it is assumed that the labeled
images are the person images within the bounding box for each individual; this
labeling across multiple nonoverlapping camera views from raw video
surveillance is costly and time-consuming. To overcome this difficulty, we
consider weakly supervised person re-id modeling. The weak setting refers to
matching a target person with an untrimmed gallery video where we only know
that the identity appears in the video without the requirement of annotating
the identity in any frame of the video during the training procedure. Hence,
for a video, there could be multiple video-level labels. We cast this weakly
supervised person re-id challenge into a multi-instance multi-label learning
(MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method
that is able to explore potential intraclass person images from all the camera
views by incorporating the intra-bag alignment and the cross-view bag
alignment. Finally, the CV-MIML method is embedded into an existing deep neural
network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have
performed extensive experiments to show the feasibility of the proposed weakly
supervised setting and verify the effectiveness of our method compared to
related methods on four weakly labeled datasets.
