Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification

04/04/2021
by   Shengcai Liao, et al.
0

Generalizable person re-identification has recently got increasing attention due to its research values as well as practical values. However, the efficiency of learning from large-scale data has not yet been much studied. In this paper, we argue that the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. Though online hard example mining improves the learning efficiency to some extent, the mining in mini batches after random sampling is still limited. Therefore, this inspires us that the hard example mining should be shifted backward to the data sampling stage. To address this, in this paper, we propose an efficient mini batch sampling method called Graph Sampling (GS) for large-scale metric learning. The basic idea is to build a nearest neighbor relationship graph for all classes at the beginning of each epoch. Then, each mini batch is composed of a randomly selected class and its nearest neighboring classes so as to provide informative and challenging examples for learning. Together with an adapted competitive baseline, we improve the previous state of the arts in generalizable person re-identification significantly, by up to 22.3 and 15 baseline by up to 4 x6.6, from 12.2 hours to 1.8 hours in training a large-scale dataset RandPerson with 8,000 IDs. Code is available at <https://github.com/ShengcaiLiao/QAConv>.

READ FULL TEXT
research
10/02/2017

Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification

Person re-identification (ReID) is an important task in computer vision....
research
05/06/2019

Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity

In this paper, we propose Hard Person Identity Mining (HPIM) that attemp...
research
11/04/2018

Deep Metric Learning by Online Soft Mining and Class-Aware Attention

Deep metric learning aims to learn a deep embedding that can capture the...
research
06/11/2020

Improving Deep Metric Learning with Virtual Classes and Examples Mining

In deep metric learning, the training procedure relies on sampling infor...
research
02/28/2023

Global Proxy-based Hard Mining for Visual Place Recognition

Learning deep representations for visual place recognition is commonly p...
research
10/26/2020

Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies

Deep metric learning plays a key role in various machine learning tasks....
research
10/23/2022

Tail Batch Sampling: Approximating Global Contrastive Losses as Optimization over Batch Assignments

Contrastive Learning has recently achieved state-of-the-art performance ...

Please sign up or login with your details

Forgot password? Click here to reset