Enhanced multi-dataset transfer learning method for unsupervised person re-identification using co-training strategy

• Published in: IET computer vision 2018-12, Vol.12 (8), p.1219-1227
• Main Authors: Xian, Yuqiao, Hu, Haifeng
• Format: Article
• Language: English
• Subjects: CNN models; co-training strategy; discriminative person representations; enhanced multidataset transfer learning method; image recognition; image representation; iterative methods; iterative training process; labelled source datasets; large-scale benchmark datasets; multiple convolutional neural network models; multiple source datasets; neural nets; pattern clustering; progressive unsupervised co-learning; Research Article; single model; soft labels; target dataset clustering; transferred models; unsupervised learning; unsupervised person re-identification
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2018.5103

This study proposes progressive unsupervised co-learning for unsupervised person re-identification by introducing a co-training strategy in an iterative training process. The authors’ method adopts an iterative training process to improve transferred models by iterating among clustering, selection, exchange, and fine-tuning. To solve the problem of transferring representations learned from multiple source datasets, their method utilises multiple convolutional neural network (CNN) models trained on different labelled source datasets by feeding soft labels obtained by clustering on target dataset to each other. The enhanced model can learn more discriminative person representations than the single model trained on multiple datasets. Experimental results on two large-scale benchmark datasets (i.e. DukeMTMC-reID and Market-1501) demonstrate that their method can enhance transferred CNN models by using more source datasets and is competitive to the state-of-the-art methods.