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：离群点检测是监督式视觉识别在安全关键应用中的核心能力。现有方法大多通过促使标准闭集模型在负训练数据上产生低置信度预测来获得最佳效果，然而该方法将预测不确定性与负类识别相混淆。为此，我们重新审视直接预测K+1个逻辑值的方案，其中K个对应真实类别，一个对应离群类别。该设定使我们能够构建一种新型异常分数，将其表述为分布内不确定性与离群类后验的集成，我们将其称为负目标性。此时离群点可通过以下两种独立机制被检测：i) 高预测不确定性，或 ii) 与负数据的相似性。我们将该方法嵌入到具有K+2类掩码级识别的密集预测架构中。训练过程促使新增的第K+2类学习粘贴负实例的负目标性。我们的模型在使用或不使用真实负数据训练的情况下，均在图像级与像素级离群点检测的标准基准测试中超越了当前最优性能。