Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift
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. We show that a model may achieve reasonable overlap while assigning the wrong semantic label, swapping one plausible foreground class for another, even when object boundaries are largely correct. We focus on this semantic label-fl...
| Authors | Akshit Achara, Yovin Yahathugoda, Nick Byrne et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 April 2026 |
| Last updated at source | 10 June 2026 |
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