Unsupervised Cross Dataset Transfer Learning for Person Re identification
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 dictionary learning method which is able to learn a dataset shared but target data biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state of the art.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)