OoDIS: Anomaly Instance Segmentation Benchmark

Alexey Nekrasov, Rui Zhou, Miriam Ackermann, Alexander Hermans, Bastian Leibe, Matthias Rottmann
VAND 2.0 Workshop at CVPR 2024

Autonomous vehicles require a precise understanding of their environment to navigate safely. Reliable identification of unknown objects, especially those that are absent during training, such as wild animals, is critical due to their potential to cause serious accidents. Significant progress in semantic segmentation of anomalies has been driven by the availability of out-of-distribution (OOD) benchmarks. However, a comprehensive understanding of scene dynamics requires the segmentation of individual objects, and thus the segmentation of instances is essential. Development in this area has been lagging, largely due to the lack of dedicated benchmarks. To address this gap, we have extended the most commonly used anomaly segmentation benchmarks to include the instance segmentation task. Our evaluation of anomaly instance segmentation methods shows that this challenge remains an unsolved problem. The benchmark website and the competition page can be found at: https://vision.rwth-aachen.de/oodis

» Show BibTeX

title = {{UGainS: Uncertainty Guided Anomaly Instance Segmentation}},
author = {Nekrasov, Alexey and Hermans, Alexander and Kuhnert, Lars and Leibe, Bastian},
booktitle = {GCPR},
year = {2023}

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