Adapt only once: Fast unsupervised person re-identification via relevance-aware guidance

Unsupervised domain adaptive person re-identification (UDA person reID) defines a task where labels in target domains are totally unknown while source domains are fully labeled. Assigning reliable labels quickly is a critical issue for UDA person reID that could be applied in the real-world scenarios. Recent studies focus on obtaining pseudo labels by clustering algorithms and then training the reID model with these labels. However, the main limitation of these methods is the high time complexity, which is caused by the calculation of all pair-wise similarities and multiple iterations in the clustering algorithm to obtain reliable results. When the data is very large or the feature dimensions are very high, the memory and time cost requirements of the clustering algorithm can increase rapidly.In this paper, we provide a fast unsupervised domain adaptive person reID framework (FUReID), which calculates the relevance between unlabeled samples only once to adapt to the new scenarios without any iterations in the stage of label generation. Especially, instead of pursuing accurate labels, FUReID considers constructing a lightweight paradigm to generate coarse labels and then refine these labels during the training stage. Therefore, FUReID designs a prototype-guided labeling method that only relies on calculating the relevance between the prototype vectors and the samples, and assigning coarse labels with noise. Then, to alleviate the issue of noise, FUReID designs a label-flexible training network with an adaptive selection strategy to refine those coarse labels progressively. For several widely-used person reID datasets, our method achieves 81.7%, 26.2%, and 87.7% in mAP on Market1501, MSMT17 and PersonX, respectively. Code is available at https://github.com/AILab90/FUReID.