A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
[article]
2018
arXiv
pre-print
In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been proposed, there has been relatively little evaluation of the influence of training data and baseline network architecture. In particular, it is usually assumed either that networks are trained on labeled data from the deployment location
arXiv:1805.06086v1
fatcat:fiarkzeidrg2babbplgnat3hgi