Divide and Conquer: Hybrid Pre-training for Person Search

• URL: http://arxiv.org/abs/2312.07970v1
• Date: Wed, 13 Dec 2023 08:33:50 GMT
• Title: Divide and Conquer: Hybrid Pre-training for Person Search
• Authors: Yanling Tian, Di Chen, Yunan Liu, Jian Yang, Shanshan Zhang
• Abstract summary: We propose a hybrid pre-training framework specifically designed for person search using sub-task data only. Our model can achieve significant improvements across diverse protocols, such as person search method, fine-tuning data, pre-training data and model backbone. Our code and pre-trained models are released for plug-and-play usage to the person search community.
• Score: 40.13016375392472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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