  Vehicle re-identification (re-id) is a fundamental problem for modern
surveillance camera networks. Existing approaches for vehicle re-id utilize
global features and local features for re-id by combining multiple subnetworks
and losses. In this paper, we propose GLAMOR, or Global and Local Attention
MOdules for Re-id. GLAMOR performs global and local feature extraction
simultaneously in a unified model to achieve state-of-the-art performance in
vehicle re-id across a variety of adversarial conditions and datasets (mAPs
80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMOR
introduces several contributions: a better backbone construction method that
outperforms recent approaches, group and layer normalization to address
conflicting loss targets for re-id, a novel global attention module for global
feature extraction, and a novel local attention module for self-guided
part-based local feature extraction that does not require supervision.
Additionally, GLAMOR is a compact and fast model that is 10x smaller while
delivering 25% better performance.
