Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision. In practice, the fully supervised methods usually require an intensive resource of manual annotation. In order to address this challenge, this work introduces a new approach to utilize existing datasets with ground truth to produce more robust predictions on unlabeled datasets, named domain adaptation, in crowd counting. While the network is trained with labeled data, samples without labels from the target domain are also added to the training process. In this process, the entropy map is computed and minimized in addition to the adversarial training process designed in parallel. Experiments on Shanghaitech, UCF_CC_50, and UCF-QNRF datasets prove a more generalized improvement of our method over the other state-of-the-arts in the cross-domain setting.

翻译：自我培训的人群计数虽然是计算机愿景的重要挑战之一,但却没有得到认真探讨。实际上,完全监督的方法通常需要大量人工说明资源。为了应对这一挑战,这项工作引入了一种新的方法,即利用现有具有地面真相的数据集,在人群计数中对未贴标签的数据集、命名领域调整作出更可靠的预测。虽然网络是用标签数据培训的,但在培训过程中也添加了没有目标领域标签的样本。在这一过程中,除平行设计的对抗性培训进程外,还计算并尽量减少了酶图。上海科技实验、UCF_CC_50和UCF-QNRF数据集证明,我们的方法比跨领域环境中的其他状态更加普遍地改进。