Context-Assisted Active Learning for Weakly Supervised Person Search

Person search is a challenging task that aims to jointly detect and identify a target person from a large-scale scene image dataset. Fully supervised person search requires both bounding boxes and person identity annotations, making it hard to deploy in real-world applications. Although recent weakly supervised person search methods can alleviate annotation workloads, they often result in severe performance degradation when compared to supervised methods. To pursue better performance with a lower annotation budget, we propose to integrate active learning into weakly supervised person search, where a small number of pairwise identity annotations are actively acquired from oracles. Specifically, we propose a context-assisted active learning framework that selects informative instance pairs for labeling and refines pseudo labels for representation learning. The proposed framework consists of a split module and a merge module, which leverage two types of contextual cues for label refinement. Besides, a pairwise relationship predictor is introduced to estimate relations between instances so that annotation cost can be further reduced. Extensive experiments demonstrate that the proposed method could achieve comparable or even better performance than recent fully supervised methods at a much lower annotation cost. Notably, our method achieves 61.4% mAP on PRW dataset, which outperforms recent fully supervised methods at a much lower annotation cost.