  Person re-identification (ReID) is aimed at identifying the same person
across videos captured from different cameras. In the view that networks
extracting global features using ordinary network architectures are difficult
to extract local features due to their weak attention mechanisms, researchers
have proposed a lot of elaborately designed ReID networks, while greatly
improving the accuracy, the model size and the feature extraction latency are
also soaring. We argue that a relatively compact ordinary network extracting
globally pooled features has the capability to extract discriminative local
features and can achieve state-of-the-art precision if only the model's
parameters are properly learnt. In order to reduce the difficulty in learning
hard identity labels, we propose a novel knowledge distillation method:
Factorized Distillation, which factorizes both feature maps and retrieval
features of holistic ReID network to mimic representations of multiple partial
ReID models, thus transferring the knowledge from partial ReID models to the
holistic network. Experiments show that the performance of model trained with
the proposed method can outperform state-of-the-art with relatively few network
parameters.
