CVPR 2023 - Reliability in Semantic Segmentation: Are We on the Right Track?

In this episode we discuss Reliability in Semantic Segmentation: Are We on the Right Track? by Pau de Jorge, Riccardo Volpi, Philip Torr, Gregory Rogez. The paper discusses a study on the reliability of modern semantic segmentation models in terms of robustness and uncertainty estimation. The authors analyze a variety of models and compare their performance on four metrics: robustness, calibration, misclassification detection, and out-of-distribution detection. They find that recent models are more robust but not more reliable in terms of uncertainty estimation, and suggest improving calibration as a way to improve other uncertainty metrics. This is the first study of its kind on modern segmentation models and is intended to assist practitioners and researchers in this fundamental vision task.