Crowd Counting with Minimal Data Using Generative Adversarial Networks for Multiple Target Regression

Abstract

In this work, we use a generative adversarial network (GAN) to train crowd counting networks using minimal data. We describe how GAN objectives can be modified to allow for the use of unlabeled data to benefit inference training in semi-supervised learning. More generally, we explain how these same methods can be used in more generic multiple regression target semi-supervised learning, with crowd counting being a demonstrative example.

Year of Publication
2018
Conference Name
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Date Published
03