Camera-Driven Representation Learning for Unsupervised Domain Adaptive Person Re-identification

Geon Lee, Sanghoon Lee, Dohyung Kim, Bumsub Ham

Abstract:

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.