This work considers supervised learning to count from images and their corresponding point annotations. Where density-based counting methods typically use the point annotations only to create Gaussian-density maps, which act as the supervision signal, the starting point of this work is that point annotations have counting potential beyond density map generation. We introduce two methods that repurpose the available point annotations to enhance counting performance. The first is a counting-specific augmentation that leverages point annotations to simulate occluded objects in both input and density images to enhance the network’s robustness to occlusions. The second method, foreground distillation, generates foreground masks from the point annotations, from which we train an auxiliary network on images with blacked-out backgrounds. By doing so, it learns to extract foreground counting knowledge without interference from the background. These methods can be seamlessly integrated with existing counting advances and are adaptable to different loss functions. We demonstrate complementary effects of the approaches, allowing us to achieve robust counting results even in challenging scenarios such as background clutter, occlusion, and varying crowd densities. Our proposed approach achieves strong counting results on multiple datasets, including ShanghaiTech Part_A and Part_B, UCF_QNRF, JHU-Crowd++, and NWPU-Crowd. Code is available at https://github.com/shizenglin/Counting-with-Focus-for-Free.

这项工作考虑监督学习从图像及其相应的点注释中进行计数。基于密度的计数方法通常仅使用点注释来创建充当监督信号的高斯密度图，这项工作的出发点是点注释具有超出密度图生成的计数潜力。我们引入了两种重新利用可用点注释来提高计数性能的方法。第一个是特定于计数的增强，它利用点注释来模拟输入图像和密度图像中的遮挡对象，以增强网络对遮挡的鲁棒性。第二种方法是前景蒸馏，从点注释生成前景掩模，我们在具有黑色背景的图像上训练辅助网络。通过这样做，它学会在不受背景干扰的情况下提取前景计数知识。这些方法可以与现有的计数技术无缝集成，并适用于不同的损失函数。我们展示了这些方法的互补效果，使我们即使在背景杂乱、遮挡和不同人群密度等具有挑战性的场景中也能获得可靠的计数结果。我们提出的方法在多个数据集上取得了良好的计数结果，包括 ShanghaiTech Part_A 和 Part_B、UCF_QNRF、JHU-Crowd++ 和 NWPU-Crowd。代码可在 https://github.com/shizenglin/Counting-with-Focus-for-Free 获取。