Camera-Driven Representation Learning for Unsupervised Domain Adaptive
  Person Re-identification
Geon Lee Sanghoon Lee Dohyung Kim Younghoon Shin Yongsang Yoon Bumsub Ham
We present a novel unsupervised domain adaption method for person
re-identification (reID) that generalizes a model trained on a labeled source
domain to an unlabeled target domain. We introduce a camera-driven curriculum
learning (CaCL) framework that leverages camera labels of person images to
transfer knowledge from source to target domains progressively. To this end, we
divide target domain dataset into multiple subsets based on the camera labels,
and initially train our model with a single subset (i.e., images captured by a
single camera). We then gradually exploit more subsets for training, according
to a curriculum sequence obtained with a camera-driven scheduling rule. The
scheduler considers maximum mean discrepancies (MMD) between each subset and
the source domain dataset, such that the subset closer to the source domain is
exploited earlier within the curriculum. For each curriculum sequence, we
generate pseudo labels of person images in a target domain to train a reID
model in a supervised way. We have observed that the pseudo labels are highly
biased toward cameras, suggesting that person images obtained from the same
camera are likely to have the same pseudo labels, even for different IDs. To
address the camera bias problem, we also introduce a camera-diversity (CD) loss
encouraging person images of the same pseudo label, but captured across various
cameras, to involve more for discriminative feature learning, providing person
representations robust to inter-camera variations. Experimental results on
standard benchmarks, including real-to-real and synthetic-to-real scenarios,
demonstrate the effectiveness of our framework.