Multi-Attribute Enhancement Network for Person Search

TL;DR

This paper introduces the Multi-Attribute Enhancement network for person search, integrating attribute learning to improve detection and re-identification accuracy in large image datasets.

Contribution

It proposes a novel model that combines global and local attribute features for enhanced person search performance.

Findings

Achieves 91.8% mAP on CUHK-SYSU dataset.

Reaches 93.0% rank-1 accuracy on CUHK-SYSU.

Outperforms existing end-to-end methods.

Abstract

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.