Deep convolutional models often produce inadequate predictions for inputs foreign to the training distribution. Consequently, the problem of detecting outlier images has recently been receiving a lot of attention. Unlike most previous work, we address this problem in the dense prediction context in order to be able to locate outlier objects in front of in-distribution background. Our approach is based on two reasonable assumptions. First, we assume that the inlier dataset is related to some narrow application field (e.g.~road driving). Second, we assume that there exists a general-purpose dataset which is much more diverse than the inlier dataset (e.g.~ImageNet-1k). We consider pixels from the general-purpose dataset as noisy negative training samples since most (but not all) of them are outliers. We encourage the model to recognize borders between known and unknown by pasting jittered negative patches over inlier training images. Our experiments target two dense open-set recognition benchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognition dataset (StreetHazard). Extensive performance evaluation indicates competitive potential of the proposed approach.

翻译：深共变模型往往对培训分布的外部投入作出不充分的预测。 因此, 探测外部图像的问题最近受到了很多关注。 与大多数先前的工作不同, 我们从密集的预测角度来解决这个问题, 以便能够在分布背景之前找到外部对象。 我们的方法基于两个合理的假设。 首先, 我们假设内向数据集与某些狭窄的应用领域( 如路口驱动) 有关。 第二, 我们假设有一个通用数据集, 其多样性远大于内向数据集( 例如 ~ ImageNet-1k ) 。 我们认为, 普通用途数据集中的像素是吵闹的负面培训样本, 因为大多数( 但并非全部)是外向的。 我们鼓励该模型识别已知和未知的边界, 因为粘贴的负向内向式的负偏差与内向培训图像有关。 我们的实验目标是两个密集的开放识别基准( WildDash 1 和 Fishesyscovers) 以及一个密集的公开识别数据集( StreetHazard) 。 广泛的绩效评估表明, 拟议的方法具有竞争性的潜力。