Divide and Conquer: Hybrid Pre-training for Person Search
Yanling Tian Di Chen Yunan Liu Jian Yang Shanshan Zhang
Large-scale pre-training has proven to be an effective method for improving
performance across different tasks. Current person search methods use ImageNet
pre-trained models for feature extraction, yet it is not an optimal solution
due to the gap between the pre-training task and person search task (as a
downstream task). Therefore, in this paper, we focus on pre-training for person
search, which involves detecting and re-identifying individuals simultaneously.
Although labeled data for person search is scarce, datasets for two sub-tasks
person detection and re-identification are relatively abundant. To this end, we
propose a hybrid pre-training framework specifically designed for person search
using sub-task data only. It consists of a hybrid learning paradigm that
handles data with different kinds of supervisions, and an intra-task alignment
module that alleviates domain discrepancy under limited resources. To the best
of our knowledge, this is the first work that investigates how to support
full-task pre-training using sub-task data. Extensive experiments demonstrate
that our pre-trained model can achieve significant improvements across diverse
protocols, such as person search method, fine-tuning data, pre-training data
and model backbone. For example, our model improves ResNet50 based NAE by 10.3%
relative improvement w.r.t. mAP. Our code and pre-trained models are released
for plug-and-play usage to the person search community.