  Person Search is designed to jointly solve the problems of Person Detection
and Person Re-identification (Re-ID), in which the target person will be
located in a large number of uncut images. Over the past few years, Person
Search based on deep learning has made great progress. Visual character
attributes play a key role in retrieving the query person, which has been
explored in Re-ID but has been ignored in Person Search. So, we introduce
attribute learning into the model, allowing the use of attribute features for
retrieval task. Specifically, we propose a simple and effective model called
Multi-Attribute Enhancement (MAE) which introduces attribute tags to learn
local features. In addition to learning the global representation of
pedestrians, it also learns the local representation, and combines the two
aspects to learn robust features to promote the search performance.
Additionally, we verify the effectiveness of our module on the existing
benchmark dataset, CUHK-SYSU and PRW. Ultimately, our model achieves
state-of-the-art among end-to-end methods, especially reaching 91.8% of mAP and
93.0% of rank-1 on CUHK-SYSU.Codes and models are available at
https://github.com/chenlq123/MAE.
