  The pre-training task is indispensable for the text-to-image person
re-identification (T2I-ReID) task. However, there are two underlying
inconsistencies between these two tasks that may impact the performance; i)
Data inconsistency. A large domain gap exists between the generic images/texts
used in public pre-trained models and the specific person data in the T2I-ReID
task. This gap is especially severe for texts, as general textual data are
usually unable to describe specific people in fine-grained detail. ii) Training
inconsistency. The processes of pre-training of images and texts are
independent, despite cross-modality learning being critical to T2I-ReID. To
address the above issues, we present a new unified pre-training pipeline
(UniPT) designed specifically for the T2I-ReID task. We first build a
large-scale text-labeled person dataset "LUPerson-T", in which pseudo-textual
descriptions of images are automatically generated by the CLIP paradigm using a
divide-conquer-combine strategy. Benefiting from this dataset, we then utilize
a simple vision-and-language pre-training framework to explicitly align the
feature space of the image and text modalities during pre-training. In this
way, the pre-training task and the T2I-ReID task are made consistent with each
other on both data and training levels. Without the need for any bells and
whistles, our UniPT achieves competitive Rank-1 accuracy of, ie, 68.50%,
60.09%, and 51.85% on CUHK-PEDES, ICFG-PEDES and RSTPReid, respectively. Both
the LUPerson-T dataset and code are available at
https;//github.com/ZhiyinShao-H/UniPT.
