Person re-identification (Re-ID) poses an inevitable challenge to deep learning: how to learn a robust deep model with millions of parameters on a small training set of few or no labels. In this paper, two deep transfer learning methods are proposed to address the training data sparsity problem, respectively from the supervised and unsupervised settings. First, a two-stepped fine-tuning strategy with proxy classifier learning is developed to transfer knowledge from auxiliary datasets. Second, given an unlabelled Re-ID dataset, an unsupervised deep transfer learning model is proposed based on a co-training strategy. Extensive experiments show that the proposed models achieve a good performance of deep Re-ID models.
The work in REF transfers the representations learned from the general classification dataset to address the data sparsity of the person re-ID problems.
Deep Transfer Learning for Person Re-Identification
2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)
2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)
Computer Science