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.

翻译：离群点检测是视觉识别安全关键应用中的核心能力。现有方法大多通过引导标准闭集模型对负训练数据产生低置信度预测来取得最佳效果，但这会将预测不确定性与负类识别混为一谈。为此，我们重新审视K+1个logits（对应K个真实类别与1个离群类）的直接预测范式。该框架使我们能提出新型异常评分——将分布内不确定性与离群类后验概率（称为负目标性）进行集成。离群点现可独立检测，依据为：i)高预测不确定性，或ii)与负数据的相似性。我们将该方法嵌入基于K+2类掩码级识别的稠密预测架构中。训练过程促使新增的第K+2类在粘贴的负实例上学习负目标性。在标准图像级与像素级离群点检测基准测试中（无论是否使用真实负数据训练），我们的模型均超越当前最优方法。