Prompting Continual Person Search

TL;DR

This paper introduces a continual learning framework for person search that adapts to multiple domains using prompt-based methods, effectively balancing learning new knowledge and retaining previous information.

Contribution

It proposes a novel prompt-based continual person search model with a compositional transformer and domain-specific prompts for effective multi-domain learning.

Findings

Outperforms existing methods on continual person search benchmarks.

Effectively balances stability and plasticity in multi-domain learning.

Demonstrates strong generalization across diverse domains.

Abstract

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 increaseing 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 Prompt-based Continual Person Search (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 projections 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 is available at https://github.com/PatrickZad/PoPS.