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Found 6 papers, 4 papers with code
Detecting Novelties with Empty Classes
We thereafter extend the DNN by $k$ empty classes and fine-tune it on the OoD data samples.
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations.
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects.
Detecting and Learning the Unknown in Semantic Segmentation
However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to.
Towards Unsupervised Open World Semantic Segmentation
More precisely, the connected components of a predicted semantic segmentation are assessed by a segmentation quality estimate.
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes.
