  This paper aims to learn a domain-generalizable (DG) person re-identification
(ReID) representation from large-scale videos \textbf{without any annotation}.
Prior DG ReID methods employ limited labeled data for training due to the high
cost of annotation, which restricts further advances. To overcome the barriers
of data and annotation, we propose to utilize large-scale unsupervised data for
training. The key issue lies in how to mine identity information. To this end,
we propose an Identity-seeking Self-supervised Representation learning (ISR)
method. ISR constructs positive pairs from inter-frame images by modeling the
instance association as a maximum-weight bipartite matching problem. A
reliability-guided contrastive loss is further presented to suppress the
adverse impact of noisy positive pairs, ensuring that reliable positive pairs
dominate the learning process. The training cost of ISR scales approximately
linearly with the data size, making it feasible to utilize large-scale data for
training. The learned representation exhibits superior generalization ability.
\textbf{Without human annotation and fine-tuning, ISR achieves 87.0\% Rank-1 on
Market-1501 and 56.4\% Rank-1 on MSMT17}, outperforming the best supervised
domain-generalizable method by 5.0\% and 19.5\%, respectively. In the
pre-training$\rightarrow$fine-tuning scenario, ISR achieves state-of-the-art
performance, with 88.4\% Rank-1 on MSMT17. The code is at
\url{https://github.com/dcp15/ISR_ICCV2023_Oral}.
