Outlier detection is an essential capability in safety-critical applications
of supervised visual recognition. Most of the existing methods deliver best
results by encouraging standard closed-set models to produce low-confidence
predictions in negative training data. However, that approach conflates
prediction uncertainty with recognition of the negative class. We therefore
reconsider direct prediction of K+1 logits that correspond to K groundtruth
classes and one outlier class. This setup allows us to formulate a novel
anomaly score as an ensemble of in-distribution uncertainty and the posterior
of the outlier class which we term negative objectness. Now outliers can be
independently detected due to i) high prediction uncertainty or ii) similarity
with negative data. We embed our method into a dense prediction architecture
with mask-level recognition over K+2 classes. The training procedure encourages
the novel K+2-th class to learn negative objectness at pasted negative
instances. Our models outperform the current state-of-the art on standard
benchmarks for image-wide and pixel-level outlier detection with and without
training on real negative data.

在具有监督的视觉识别的安全关键应用中，离群点检测是一项至关重要的能力。本文提出一种新颖的方法，将异常分数定义为内部不确定性与离群类别的后验的集合，从而实现离群点的独立检测。该方法嵌入到一个密集预测架构中，并在 K+2 个类别上进行掩膜级别识别。通过训练过程，鼓励第 K+2 个类别在负面实例中学习负性关注度。我们的模型在标准基准上表现优于当前最先进的图像全局和像素级离群点检测方法，无论是否在真实负面数据上进行训练。