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，但该方法将预测不确定性与负类识别混为一谈。因此，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。该设定允许我们将新颖的异常分数构建为分布内不确定性与离群类后验的集成，后者被我们称为负目标性。此时离群点可因以下任一情况被独立检测：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类在粘贴的负实例上学习负目标性。我们的模型在标准基准测试中，无论是否使用真实负数据进行训练，均在图像级与像素级离群点检测任务上超越了当前最优方法。