Object counting typically uses 2D point annotations. The complexity of object shapes and the subjectivity of annotators may lead to annotation inconsistency, potentially confusing counting model training. Some sophisticated noise-resistance counting methods have been proposed to alleviate this issue. Differently, we aim to directly refine the initial point annotations before training counting models. For that, we propose the Shifted Autoencoders (SAE), which enhances annotation consistency. Specifically, SAE applies random shifts to initial point annotations and employs a UNet to restore them to their original positions. Similar to MAE reconstruction, the trained SAE captures general position knowledge and ignores specific manual offset noise. This allows to restore the initial point annotations to more general and thus consistent positions. Extensive experiments show that using such refined consistent annotations to train some advanced (including noise-resistance) object counting models steadily/significantly boosts their performances. Remarkably, the proposed SAE helps to set new records on nine datasets. We will make codes and refined point annotations available.

翻译：目标计数通常使用二维点标注。物体形状的复杂性和标注人员的主观性可能导致标注不一致，从而干扰计数模型的训练。已有一些复杂的抗噪声计数方法被提出来缓解这一问题。与之不同，本文旨在直接改进训练计数模型前的初始点标注。为此，我们提出移位自编码器（SAE），以增强标注一致性。具体而言，SAE对初始点标注施加随机位移，并利用UNet将其恢复至原始位置。与MAE重建类似，训练后的SAE能够捕捉位置的一般性先验知识，同时忽略特定的手动偏移噪声。这使得初始点标注能够被恢复至更通用且更一致的位置。大量实验表明，使用这种经过精炼的一致标注来训练先进的（包括抗噪声）目标计数模型，能够稳定/显著提升其性能。值得注意的是，所提出的SAE在九个数据集上帮助刷新了记录。我们将公开代码和精炼后的点标注。