In this paper, we tackle the detection of out-of-distribution (OOD) objects
in semantic segmentation. By analyzing the literature, we found that current
methods are either accurate or fast but not both which limits their usability
in real world applications. To get the best of both aspects, we propose to
mitigate the common shortcomings by following four design principles:
decoupling the OOD detection from the segmentation task, observing the entire
segmentation network instead of just its output, generating training data for
the OOD detector by leveraging blind spots in the segmentation network and
focusing the generated data on localized regions in the image to simulate OOD
objects. Our main contribution is a new OOD detection architecture called
ObsNet associated with a dedicated training scheme based on Local Adversarial
Attacks (LAA). We validate the soundness of our approach across numerous
ablation studies. We also show it obtains top performances both in speed and
accuracy when compared to ten recent methods of the literature on three
different datasets.

本文提出了一种名为 ObsNet 的 OOD 检测体系结构，通过基于局部对抗攻击（LAA）的专用训练方案，实现了在速度和精度方面得到表现的最佳方法，解决了当前方法在实际应用中速度和精度快慢两难的问题。