Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift

ArXi:2604.13326v1 Announce Type: new The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such correlations is to train on data where the label is strongly tied to some non-causal cue, then evaluate on examples where that tie no longer holds. This idea is well established for classification tasks, but for semantic segmentation the specific failure modes are not well understood.