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Svenja Uhlemeyer

2 accepted papers

2022

Towards unsupervised open world semantic segmentation

UAI 2022poster

For the semantic segmentation of images, state-of-the-art deep neural networks (DNNs) achieve high segmentation accuracy if that task is restricted to a closed set of classes. However, as of now DNNs have limited ability to operate in an open world, where they are tasked to identify pixels belonging…

2021

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

NeurIPS 2021poster

State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle previously-unseen objects. However, detecting and localizing such objects is crucial for safety-critical applications such…

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