Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)

Date

2018

Authors

Sun, Yifan
Zheng, Liang
Yang, Yi
Tian, Qi
Wang, Shengjin

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

Abstract

Employing part-level features offers fine-grained information for pedestrian image description. A prerequisite of part discovery is that each part should be well located. Instead of using external resources like pose estimator, we consider content consistency within each part for precise part location. Specifically, we target at learning discriminative part-informed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin. Code is available at: https://github.com/syfafterzy/PCB_RPP

Description

Keywords

Person retrieval, Part-level feature, Part refinement

Citation

Source

Proceedings of the 15th European Conference on Computer Vision, ECCV 2018

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1007/978-3-030-01225-0_30

Restricted until

2099-12-31