In recent years, deep neural networks have defined the state-of-the-art in semantic segmentation where their predictions are constrained to a predefined set of semantic classes. They are to be deployed in applications such as automated driving, although their categorically confined expressive power runs contrary to such open world scenarios. Thus, the detection and segmentation of objects from outside their predefined semantic space, i.e., out-of-distribution (OoD) objects, is of highest interest. Since uncertainty estimation methods like softmax entropy or Bayesian models are sensitive to erroneous predictions, these methods are a natural baseline for OoD detection. Here, we present a method for obtaining uncertainty scores from pixel-wise loss gradients which can be computed efficiently during inference. Our approach is simple to implement for a large class of models, does not require any additional training or auxiliary data and can be readily used on pre-trained segmentation models. Our experiments show the ability of our method to identify wrong pixel classifications and to estimate prediction quality at negligible computational overhead. In particular, we observe superior performance in terms of OoD segmentation to comparable baselines on the SegmentMeIfYouCan benchmark, clearly outperforming other methods.

翻译：近年来，深度神经网络在语义分割领域确立了最先进的水平，其预测受限于预定义的语义类别集合。这些模型将被部署于自动驾驶等应用中，但其类别有限的表达能力与这类开放世界场景存在矛盾。因此，检测并分割超出预定义语义空间的对象（即分布外对象）具有极高价值。由于softmax熵或贝叶斯模型等不确定性估计方法对错误预测敏感，这些方法自然成为分布外检测的基准。本文提出一种从逐像素损失梯度中获取不确定性分数的方法，该方法可在推理阶段高效计算。我们的方法易于实现，适用于大规模模型，无需额外训练或辅助数据，可直接用于预训练的分割模型。实验表明，该方法能以可忽略的计算开销识别错误的像素分类并评估预测质量。特别地，在SegmentMeIfYouCan基准测试中，我们在分布外分割任务上取得了优于可比基线方法的性能，明显超越其他方法。