Interpreting camera data is key for autonomously acting systems, such as
autonomous vehicles. Vision systems that operate in real-world environments
must be able to understand their surroundings and need the ability to deal with
novel situations. This paper tackles open-world semantic segmentation, i.e.,
the variant of interpreting image data in which objects occur that have not
been seen during training. We propose a novel approach that performs accurate
closed-world semantic segmentation and, at the same time, can identify new
categories without requiring any additional training data. Our approach
additionally provides a similarity measure for every newly discovered class in
an image to a known category, which can be useful information in downstream
tasks such as planning or mapping. Through extensive experiments, we show that
our model achieves state-of-the-art results on classes known from training data
as well as for anomaly segmentation and can distinguish between different
unknown classes.

该论文提出了一种新颖的方法，能够在没有额外训练数据的情况下，同时完成准确的封闭世界语义分割和识别新类别，还提供了每个新发现类别与已知类别的相似度度量，可在规划或映射等下游任务中提供有用信息。通过广泛的实验，我们展示了我们的模型在训练数据中已知类别以及异常分割方面取得了最先进的结果，并且能够区分不同的未知类别。