论文标题
人群计数的渐进多分辨率损失
Progressive Multi-resolution Loss for Crowd Counting
论文作者
论文摘要
人群计数通常以密度图回归方式处理，该方式通过预测的密度图和地面真相之间的L2损失进行监督。为了有效调节模型，已经提出了各种改进的L2损耗函数，以找到预测的密度和注释位置之间的更好对应关系。在本文中，我们建议以一个分辨率预测密度图，但在多个分辨率下测量密度图。通过在这种情况下最大化后验概率，我们获得了对数形成的多分辨率L2差异损失，其中传统的单分辨率L2损失是其特殊情况。我们从数学上证明它优于单分辨率L2损失。没有铃铛和口哨声，与四个人群计数数据集的最新方法相比，拟议的损失显着改善了几个基线，并表现出色，Shanghaitech A＆B，UCF-QNRF和Jhu-Crowd ++。
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate models, various improved L2 loss functions have been proposed to find a better correspondence between predicted density and annotation positions. In this paper, we propose to predict the density map at one resolution but measure the density map at multiple resolutions. By maximizing the posterior probability in such a setting, we obtain a log-formed multi-resolution L2-difference loss, where the traditional single-resolution L2 loss is its particular case. We mathematically prove it is superior to a single-resolution L2 loss. Without bells and whistles, the proposed loss substantially improves several baselines and performs favorably compared to state-of-the-art methods on four crowd counting datasets, ShanghaiTech A & B, UCF-QNRF, and JHU-Crowd++.
