论文标题
人群计数中的自我监督领域的适应
Self-supervised Domain Adaptation in Crowd Counting
论文作者
论文摘要
自我训练的人群计数并未专心探索，尽管这是计算机视觉中的重要挑战之一。实际上，完全监督的方法通常需要大量的手动注释资源。为了应对这一挑战，这项工作引入了一种新方法，以利用基本真理的现有数据集，以在人群计数中对未标记的数据集（名为域名适应）产生更强大的预测。虽然网络接受了标记的数据训练，但培训过程中还添加了来自目标域的标签的样品。在此过程中，除了平行设计的对抗训练过程外，还计算和最小化熵图。 Shanghaitech，UCF_CC_50和UCF-QNRF数据集的实验证明，在跨域设置中，我们的方法对我们的方法进行了更广泛的改进。
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.
