Unknown Instance Learning for Person Search

Although person search has achieved great progress with advanced deep neural networks, it remains an intricate challenge, necessitating a concurrent solution to pedestrian detection and person re-identification. Due to the difficulty of accurate labeling, the current widely used person search datasets contain numerous unknown identities in addition to the carefully labeled known ones. However, previous efforts usually simply utilize these unknown persons as negative samples, inadvertently disregarding the wealth of untapped sample information. To tackle this issue, we propose a novel Unknown Instance Learning (UIL) method that introduces an extra network branch to mine the potential identity information of unknown individuals during training, apart from the fundamental search branch. Specifically, a Clustering-Based Unknowns Labeling (CBUL) mechanism is introduced to effectively exploit the features extracted from unknown persons. In addition, recognizing the distinct roles of pseudo and real identity labels in discriminative feature learning, we design a Reformative Online Instance Matching (ROIM) loss and maintain lookup tables for real and pseudo labels separately. Extensive experiments on the CUHK-SYSU and PRW datasets demonstrate the superiority of our approach in comparison to other state-of-the-art person search models.