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Vehicle re-identification (re-id) is a fundamental problem for modernsurveillance camera networks. Existing approaches for vehicle re-id utilizeglobal features and local features for re-id by combining multiple subnetworksand losses. In this paper, we propose GLAMOR, or Global and Local AttentionMOdules for Re-id. GLAMOR performs global and local feature extractionsimultaneously in a unified model to achieve state-of-the-art performance invehicle re-id across a variety of adversarial conditions and datasets (mAPs80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMORintroduces several contributions: a better backbone construction method thatoutperforms recent approaches, group and layer normalization to addressconflicting loss targets for re-id, a novel global attention module for globalfeature extraction, and a novel local attention module for self-guidedpart-based local feature extraction that does not require supervision.Additionally, GLAMOR is a compact and fast model that is 10x smaller whiledelivering 25% better performance.

Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention