Distribution Segmentation

Distribution segmentation focuses on identifying and segmenting objects in images that fall outside the categories a model was trained on (out-of-distribution or OoD objects). Current research emphasizes improving the accuracy and efficiency of OoD detection, often employing techniques like likelihood ratio-based scoring, contextual information aggregation across multiple scales, and instance-aware anomaly detection. These advancements are crucial for deploying robust semantic segmentation models in real-world applications like autonomous driving and robotics, where encountering unexpected objects is inevitable. The ultimate goal is to create systems that can reliably segment both known and unknown objects, enhancing safety and performance in open-world scenarios.