Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification.

Thanks to the advent of deep neural networks, recent years have witnessed rapid progress in person re-identification (re-ID). Deep-learning-based methods dominate the leadership of large-scale benchmarks, some of which even surpass the human-level performance. Despite their impressive performance under the single-domain setup, current fully-supervised re-ID models degrade significantly when transplanted to an unseen domain.