Gradual Domain Adaptation with Sample Transferability Exploitation for Person Re-Identification

Zong Guo, Bingpeng Ma, Hong Chang, Xilin Chen

Abstract: In this paper, we propose a novel gradual domain adaptation method with sample transferability exploitation to tackle the unsupervised domain adaptation (UDA) for person re-identification (re-id). Due to the direct but rough adaptation scheme, existing UDA for person re-id methods usually suffer from source domain-specific characteristics. To filter out the source domain-specific characteristics, motivated by the curriculum learning strategy, we conduct gradual domain adaptation by domain-level re-weighting with polynomial weight decay. Furthermore, we exploit sample transferability via maximum mean discrepancy based sample-level re-weighting strategy to diminish the domain gap. The sample transferability exploitation spotlights samples with higher importance to the adaptation process in each domain, hence enhance the adaptation performance. By combining the gradual domain adaptation with the sample transferability exploitation, our method achieves the state-of-the-art performance on transferring between two common person re-id datasets.