论文标题
实例感知的观察者网络，用于分布外对象细分
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation
论文作者
论文摘要
关于预测不确定性估计的最新研究表明，用于语义分割的分布（OOD）检测结果有希望的结果。但是，这些方法难以精确地定位图像中的兴趣点，即异常。这种限制是由于像素水平上细化的预测难度。为了解决这个问题，我们通过向观察者提供对象实例知识来构建最新的观察者方法。我们通过利用实例掩码预测来扩展观察网。我们使用其他类别的对象检测器来过滤和汇总观察者预测。最后，我们预测图像中每个实例的唯一异常得分。我们表明，我们提出的方法准确地从三个数据集上的OOD对象确定了分布对象。
Recent works on predictive uncertainty estimation have shown promising results on Out-Of-Distribution (OOD) detection for semantic segmentation. However, these methods struggle to precisely locate the point of interest in the image, i.e, the anomaly. This limitation is due to the difficulty of finegrained prediction at the pixel level. To address this issue, we build upon the recent ObsNet approach by providing object instance knowledge to the observer. We extend ObsNet by harnessing an instance-wise mask prediction. We use an additional, class agnostic, object detector to filter and aggregate observer predictions. Finally, we predict an unique anomaly score for each instance in the image. We show that our proposed method accurately disentangles in-distribution objects from OOD objects on three datasets.
