@inproceedings{
zhang2024prompting,
title={Prompting Continual Person Search},
author={Pengcheng Zhang and Xiaohan Yu and Xiao Bai and Jin Zheng and Xin Ning},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=uCvsmnTMJg}
}
The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increasing real-world data and adaptively process input from different domains. To this end, this work introduces the continual person search task that sequentially learns on multiple domains and then performs person search on all seen domains. This requires balancing the stability and plasticity of the model to continually learn new knowledge without catastrophic forgetting. For this, we propose a \textbf{P}rompt-based C\textbf{o}ntinual \textbf{P}erson \textbf{S}earch (PoPS) model in this paper. First, we design a compositional person search transformer to construct an effective pre-trained transformer without exhaustive pre-training from scratch on large-scale person search data. This serves as the fundamental for prompt-based continual learning. On top of that, we design a domain incremental prompt pool with a diverse attribute matching module. For each domain, we independently learn a set of prompts to encode the domain-oriented knowledge. Meanwhile, we jointly learn a group of diverse attribute projection and prototype embeddings to capture discriminative domain attributes. By matching an input image with the learned attributes across domains, the learned prompts can be properly selected for model inference. Extensive experiments are conducted to validate the proposed method for continual person search. The source code will be made available upon publication.
Prompting Continual Person Search
Pengcheng Zhang Xiaohan Yu Xiao Bai Jin Zheng Xin Ning