Multi-attribute Enhancement Network For Person Search

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. Ulti

Stats

• citations5
• S2 citations—
• github stars9
• HF likes0
• heat score7.84
• arxiv keychen2021multi

Related papers

• Decoupled And Memory-reinforced Networks: Towards Effective Feature Learning For One-step Person Search (2021)12.25
• Improving Person Re-identification By Attribute And Identity Learning (2017)20.71
• Learning Context-aware Embedding For Person Search (2021)0.00
• Deep-person: Learning Discriminative Deep Features For Person Re-identification (2017)16.90
• Neighbor-based Feature And Index Enhancement For Person Re-identification (2025)0.00
• Adaptive Re-ranking Of Deep Feature For Person Re-identification (2018)0.00
• Maskreid: A Mask Based Deep Ranking Neural Network For Person Re-identification (2018)0.00
• ASMR: Learning Attribute-based Person Search With Adaptive Semantic Margin Regularizer (2021)10.07