  Group re-identification (G-ReID) is an important yet less-studied task. Its
challenges not only lie in appearance changes of individuals which have been
well-investigated in general person re-identification (ReID), but also derive
from group layout and membership changes. So the key task of G-ReID is to learn
representations robust to such changes. To address this issue, we propose a
Transferred Single and Couple Representation Learning Network (TSCN). Its
merits are two aspects: 1) Due to the lack of labelled training samples,
existing G-ReID methods mainly rely on unsatisfactory hand-crafted features. To
gain the superiority of deep learning models, we treat a group as multiple
persons and transfer the domain of a labeled ReID dataset to a G-ReID target
dataset style to learn single representations. 2) Taking into account the
neighborhood relationship in a group, we further propose learning a novel
couple representation between two group members, that achieves more
discriminative power in G-ReID tasks. In addition, an unsupervised weight
learning method is exploited to adaptively fuse the results of different views
together according to result patterns. Extensive experimental results
demonstrate the effectiveness of our approach that significantly outperforms
state-of-the-art methods by 11.7\% CMC-1 on the Road Group dataset and by
39.0\% CMC-1 on the DukeMCMT dataset.
