Learning from Web Data: Improving Crowd Counting via Semi-Supervised Learning
Tao Peng, Rong Li, Shang Li, Pengfei Zhu · 2021
Deep neural networks have been widely used in crowd counting that aims to give the number of objects in images and videos. The performance of crowd counting models is greatly affected by the size of datasets with high quality annotations. However, collecting and annotating large-scale crowd counting dataset is labor-intensive and time-consuming. In this work, we exploit unlabeled web images to boost the performance of crowd counting models in a semi-supervised manner. Based on the observation that the rotation and splitting operations will not change the number of object in image, we design three auxiliary tasks to improve the feature representation ability of deep models. A semi-supervised multi-task learning framework is proposed by introducing three auxiliary tasks with respect to unlabeled data and our framework can be easily extended to other crowd counting models. An unlabeled dataset (Web-Crowd) with 8679 web images are collected for semi-supervised crowd counting. Experiments shows that our semi-supervised multi-task learning framework can effectively boost the performance of crowd counting models on UCF-QNRF dataset and ShanghaiTech dataset.