  In a real world environment, person re-identification (Re-ID) is a
challenging task due to variations in lighting conditions, viewing angles, pose
and occlusions. Despite recent performance gains, current person Re-ID
algorithms still suffer heavily when encountering these variations. To address
this problem, we propose a semantic consistency and identity mapping
multi-component generative adversarial network (SC-IMGAN) which provides style
adaptation from one to many domains. To ensure that transformed images are as
realistic as possible, we propose novel identity mapping and semantic
consistency losses to maintain identity across the diverse domains. For the
Re-ID task, we propose a joint verification-identification quartet network
which is trained with generated and real images, followed by an effective
quartet loss for verification. Our proposed method outperforms state-of-the-art
techniques on six challenging person Re-ID datasets: CUHK01, CUHK03, VIPeR,
PRID2011, iLIDS and Market-1501.
