论文标题
随机YOLO：数据集偏移下有效的概率对象检测
Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts
论文作者
论文摘要
在图像分类任务中，很好地研究了模型对增加数据集偏移的鲁棒性的鲁棒性评估。但是，对象检测（OD）任务对不确定性估计和评估提出了其他挑战。例如，需要评估标签不确定性（即什么？）和空间不确定性（即在哪里？）的标签质量，但对于给定的边界框，无法使用更传统的平均精度度量（例如，地图）进行评估。在本文中，我们通过以Monte Carlo辍学（MC-Drop）的形式引入随机性来调整良好的Yolov3体系结构，以产生不确定性估计，并在不同级别的数据集转移中对其进行评估。我们称这种新颖的体系结构随机Yolo，并提供有效的实施，以有效地减少推理时MC-Drop采样机制的负担。最后，我们提供了一些灵敏度分析，同时认为随机Yolo是一种合理的方法，可以改善不确定性估计的不同组成部分，特别是空间不确定性。
In image classification tasks, the evaluation of models' robustness to increased dataset shifts with a probabilistic framework is very well studied. However, object detection (OD) tasks pose other challenges for uncertainty estimation and evaluation. For example, one needs to evaluate both the quality of the label uncertainty (i.e., what?) and spatial uncertainty (i.e., where?) for a given bounding box, but that evaluation cannot be performed with more traditional average precision metrics (e.g., mAP). In this paper, we adapt the well-established YOLOv3 architecture to generate uncertainty estimations by introducing stochasticity in the form of Monte Carlo Dropout (MC-Drop), and evaluate it across different levels of dataset shift. We call this novel architecture Stochastic-YOLO, and provide an efficient implementation to effectively reduce the burden of the MC-Drop sampling mechanism at inference time. Finally, we provide some sensitivity analyses, while arguing that Stochastic-YOLO is a sound approach that improves different components of uncertainty estimations, in particular spatial uncertainties.
