  Person search unifies person detection and person re-identification (Re-ID)
to locate query persons from the panoramic gallery images. One major challenge
comes from the imbalanced long-tail person identity distributions, which
prevents the one-step person search model from learning discriminative person
features for the final re-identification. However, it is under-explored how to
solve the heavy imbalanced identity distributions for the one-step person
search. Techniques designed for the long-tail classification task, for example,
image-level re-sampling strategies, are hard to be effectively applied to the
one-step person search which jointly solves person detection and Re-ID subtasks
with a detection-based multi-task framework. To tackle this problem, we propose
a Subtask-dominated Transfer Learning (STL) method. The STL method solves the
long-tail problem in the pretraining stage of the dominated Re-ID subtask and
improves the one-step person search by transfer learning of the pretrained
model. We further design a Multi-level RoI Fusion Pooling layer to enhance the
discrimination ability of person features for the one-step person search.
Extensive experiments on CUHK-SYSU and PRW datasets demonstrate the superiority
and effectiveness of the proposed method.
