  Long-term Person Re-Identification (LRe-ID) aims at matching an individual
across cameras after a long period of time, presenting variations in clothing,
pose, and viewpoint. In this work, we propose CCPA: Contrastive Clothing and
Pose Augmentation framework for LRe-ID. Beyond appearance, CCPA captures body
shape information which is cloth-invariant using a Relation Graph Attention
Network. Training a robust LRe-ID model requires a wide range of clothing
variations and expensive cloth labeling, which is lacked in current LRe-ID
datasets. To address this, we perform clothing and pose transfer across
identities to generate images of more clothing variations and of different
persons wearing similar clothing. The augmented batch of images serve as inputs
to our proposed Fine-grained Contrastive Losses, which not only supervise the
Re-ID model to learn discriminative person embeddings under long-term scenarios
but also ensure in-distribution data generation. Results on LRe-ID datasets
demonstrate the effectiveness of our CCPA framework.
