Name
Abstract | ||
|---|---|---|
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-Iddataset, 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BigMM.2018.8499067 | 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) |
Keywords | DocType | ISBN |
Person Re-ID,Deep Transfer Learning,Unsupervised Learning | Conference | 978-1-5386-5322-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoran Chen | 1 | 18 | 6.89 |
| Yaowei Wang | 2 | 134 | 29.62 |
| Yemin Shi | 3 | 37 | 9.48 |
| Yan Ke | 4 | 2581 | 191.93 |
| Geng Mengyue | 5 | 6 | 1.90 |
| Yonghong Tian | 6 | 1057 | 102.81 |
| Tao Xiang | 7 | 4929 | 215.84 |