Common object detection models consist of classification and regression branches, due to different task drivers, these two branches have different sensibility to the features from the same scale level and the same spatial location. The point-based prediction method, which is based on the assumption that the high classification confidence point has the high regression quality, leads to the misalignment problem. Our analysis shows, the problem is further composed of scale misalignment and spatial misalignment specifically. We aim to resolve the phenomenon at minimal cost: a minor adjustment of the head network and a new label assignment method replacing the rigid one. Our experiments show that, compared to the baseline FCOS, a one-stage and anchor-free object detection model, our model consistently get around 3 AP improvement with different backbones, demonstrating both simplicity and efficiency of our method.

翻译：由于任务驱动因素不同,这两个分支对同一规模水平和同一空间位置的特征具有不同感知性。基于点的预测方法基于高分类信任点具有高回归质量的假设,导致不匹配问题。我们的分析表明,问题还进一步包括规模不匹配和空间不匹配。我们的目标是以最低成本解决这一现象:对主网络进行微小调整和采用新的标签分配方法来取代僵硬方法。我们的实验表明,与基线FCOS相比,我们模型是一个单级和无锚物体探测模型,一个阶段和无锚物体探测模型,始终以不同的骨干在3个AP上得到改进,显示了我们方法的简单性和效率。