  Person re-identification is the challenging task of identifying a person
across different camera views. Training a convolutional neural network (CNN)
for this task requires annotating a large dataset, and hence, it involves the
time-consuming manual matching of people across cameras. To reduce the need for
labeled data, we focus on a semi-supervised approach that requires only a
subset of the training data to be labeled. We conduct a comprehensive survey in
the area of person re-identification with limited labels. Existing works in
this realm are limited in the sense that they utilize features from multiple
CNNs and require the number of identities in the unlabeled data to be known. To
overcome these limitations, we propose to employ part-based features from a
single CNN without requiring the knowledge of the label space (i.e., the number
of identities). This makes our approach more suitable for practical scenarios,
and it significantly reduces the need for computational resources. We also
propose a PartMixUp loss that improves the discriminative ability of learned
part-based features for pseudo-labeling in semi-supervised settings. Our method
outperforms the state-of-the-art results on three large-scale person re-id
datasets and achieves the same level of performance as fully supervised methods
with only one-third of labeled identities.
