  Most existing person re-identification (Re-ID) approaches follow a supervised
learning framework, in which a large number of labelled matching pairs are
required for training. Such a setting severely limits their scalability in
real-world applications where no labelled samples are available during the
training phase. To overcome this limitation, we develop a novel unsupervised
Multi-task Mid-level Feature Alignment (MMFA) network for the unsupervised
cross-dataset person re-identification task. Under the assumption that the
source and target datasets share the same set of mid-level semantic attributes,
our proposed model can be jointly optimised under the person's identity
classification and the attribute learning task with a cross-dataset mid-level
feature alignment regularisation term. In this way, the learned feature
representation can be better generalised from one dataset to another which
further improve the person re-identification accuracy. Experimental results on
four benchmark datasets demonstrate that our proposed method outperforms the
state-of-the-art baselines.
