论文标题
对象检测和分割的置信校准
Confidence Calibration for Object Detection and Segmentation
论文作者
论文摘要
从神经网络获得的校准置信度估计是至关重要的，尤其是对于诸如自主驾驶或医疗图像诊断之类的安全性应用程序。但是，尽管已经研究了有关分类问题的置信度校准任务，但仍缺少有关对象检测和分割问题的详尽研究。因此，我们专注于本章中对象检测和分割模型的置信度校准的研究。我们介绍了多元置信校准的概念，这是对象检测和分割任务的众所周知校准方法的扩展。这允许进行扩展的置信校准，还知道其他功能，例如边界框/像素位置，形状信息等。此外，我们扩展了预期的校准误差（ECE），以测量对象检测和分割模型的错误计算。我们检查了MS Coco以及CityScapes上的几个网络体系结构，并表明鉴于引入的校准定义，尤其是对象检测以及实例分割模型在本质上误解了。使用我们提出的校准方法，我们能够改善校准，从而对分割面罩的质量也产生积极影响。
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-known calibration methods to the task of object detection and segmentation. This allows for an extended confidence calibration that is also aware of additional features such as bounding box/pixel position, shape information, etc. Furthermore, we extend the expected calibration error (ECE) to measure miscalibration of object detection and segmentation models. We examine several network architectures on MS COCO as well as on Cityscapes and show that especially object detection as well as instance segmentation models are intrinsically miscalibrated given the introduced definition of calibration. Using our proposed calibration methods, we have been able to improve calibration so that it also has a positive impact on the quality of segmentation masks as well.
