  Most person re-identification methods, being supervised techniques, suffer
from the burden of massive annotation requirement. Unsupervised methods
overcome this need for labeled data, but perform poorly compared to the
supervised alternatives. In order to cope with this issue, we introduce the
problem of learning person re-identification models from videos with weak
supervision. The weak nature of the supervision arises from the requirement of
video-level labels, i.e. person identities who appear in the video, in contrast
to the more precise framelevel annotations. Towards this goal, we propose a
multiple instance attention learning framework for person re-identification
using such video-level labels. Specifically, we first cast the video person
re-identification task into a multiple instance learning setting, in which
person images in a video are collected into a bag. The relations between videos
with similar labels can be utilized to identify persons, on top of that, we
introduce a co-person attention mechanism which mines the similarity
correlations between videos with person identities in common. The attention
weights are obtained based on all person images instead of person tracklets in
a video, making our learned model less affected by noisy annotations. Extensive
experiments demonstrate the superiority of the proposed method over the related
methods on two weakly labeled person re-identification datasets.
