Anomaly Aware Semantic Segmentation

Anomaly-aware semantic segmentation aims to equip standard semantic segmentation models with the ability to identify and segment objects or regions that fall outside the categories learned during training. Current research focuses on generating synthetic out-of-distribution data to augment training sets, improving pixel grouping techniques for accurate instance segmentation of anomalies, and adapting loss functions to better handle the unique challenges of anomaly detection in segmentation tasks. This field is crucial for applications like autonomous driving and medical image analysis, where robust handling of unexpected events or pathologies is paramount, improving the safety and reliability of these systems.