Deep Transfer Learning for Person Re-identification

Understand

Person re-identification (Re-ID) poses a unique challenge to deep learning: how to learn a deep model with millions of parameters on a small training set of few or no labels.

• In this paper, a number of deep transfer learning models are proposed to address the data sparsity problem.
• First, a deep network architecture is designed which differs from existing deep Re-ID models in that (a) it is more suitable for transferring representations learned from large image classification datasets, and (b) classification loss and verification loss are combined, each of which adopts a different dropout strategy.
• Second, a two-stepped fine-tuning strategy is developed to transfer knowledge from auxiliary datasets.