  In text-based person search endeavors, data generation has emerged as a
prevailing practice, addressing concerns over privacy preservation and the
arduous task of manual annotation. Although the number of synthesized data can
be infinite in theory, the scientific conundrum persists that how much
generated data optimally fuels subsequent model training. We observe that only
a subset of the data in these constructed datasets plays a decisive role.
Therefore, we introduce a new Filtering-WoRA paradigm, which contains a
filtering algorithm to identify this crucial data subset and WoRA (Weighted
Low-Rank Adaptation) learning strategy for light fine-tuning. The filtering
algorithm is based on the cross-modality relevance to remove the lots of coarse
matching synthesis pairs. As the number of data decreases, we do not need to
fine-tune the entire model. Therefore, we propose a WoRA learning strategy to
efficiently update a minimal portion of model parameters. WoRA streamlines the
learning process, enabling heightened efficiency in extracting knowledge from
fewer, yet potent, data instances. Extensive experimentation validates the
efficacy of pretraining, where our model achieves advanced and efficient
retrieval performance on challenging real-world benchmarks. Notably, on the
CUHK-PEDES dataset, we have achieved a competitive mAP of 67.02% while reducing
model training time by 19.82%.
