Preserving knowledge from the source domain for cross-domain person re-identification

Although recent cross-domain person re-identification approaches have obtained great progress, they still suffer from two core issues. The first one is the insufficient useful knowledge transfer, which means the beneficial knowledge learned from the source domain is not utilized fully due to the fine-tuning process of the two-stage training especially. The second problem is the inappropriate transfer of the source domain knowledge. Concretely, this knowledge is not distinguished before being transferred, leading to the domain-specific knowledge is detrimental to the target domain performance. To circumvent them, we design a novel collaborative learning method named Preserving Knowledge from the Source Domain (PKSD) from both instance and pixel levels, composed of Ranking-guided Instance Selection (RIS) and Projection based Gradient Selection (PGS). Firstly, the collaborative learning manner could safeguard sufficient knowledge transfer from the source domain. Additionally, RIS tries to select reliable and informative samples from the source domain dataset for training to provide sufficient domain-shared knowledge at the instance level. Subsequently, PGS fine-tunes the feature maps of the selected samples according to the gradient modifying at the pixel level of feature maps to suppress remaining domain-specific knowledge from the source domain. Experiments show that PKSD outperforms existing state-of-the-art methods.