Noised Autoencoders for Point Annotation Restoration in Object Counting

Object counting is a field of growing importance in domains such as security surveillance, urban planning, and biology. The annotation is usually provided in terms of 2D points. However, the complexity of object shapes and subjective of annotators may lead to annotation inconsistency, potentially confusing the model during training. To alleviate this issue, we introduce the Noised Autoencoders (NAE) methodology, which extracts general positional knowledge from all annotations. The method involves adding random offsets to initial point annotations, followed by a UNet to restore them to their original positions. Similar to MAE, NAE faces challenges in restoring non-generic points, necessitating reliance on the most common positions inferred from general knowledge. This reliance forms the cornerstone of our method's effectiveness. Different from existing noise-resistance methods, our approach focus on directly improving initial point annotations. Extensive experiments show that NAE yields more consistent annotations compared to the original ones, steadily enhancing the performance of advanced models trained with these revised annotations. \textbf{Remarkably, the proposed approach helps to set new records in nine datasets}. We will make the NAE codes and refined point annotations available.