  Person re-identification (re-ID) has received great success with the
supervised learning methods. However, the task of unsupervised cross-domain
re-ID is still challenging. In this paper, we propose a Hard Samples
Rectification (HSR) learning scheme which resolves the weakness of original
clustering-based methods being vulnerable to the hard positive and negative
samples in the target unlabelled dataset. Our HSR contains two parts, an
inter-camera mining method that helps recognize a person under different views
(hard positive) and a part-based homogeneity technique that makes the model
discriminate different persons but with similar appearance (hard negative). By
rectifying those two hard cases, the re-ID model can learn effectively and
achieve promising results on two large-scale benchmarks.
