Unsupervised Cross-Dataset Transfer Learning for Person Re-identification

Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task
more » ... nary learning method which is able to learn a dataset-shared but targetdata-biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state-of-the-art.
doi:10.1109/cvpr.2016.146 dblp:conf/cvpr/PengXWPGHT16 fatcat:u56wbkzymve27l4mmcepzcglh4