On the Relaxation of Conditional Independence Assumption for Image Segmentation
In semantic segmentation, a recent line of RankSEG methods directly optimizes Dice/IoU scores at inference time, improving alignment with evaluation metrics without modifying model training. Despite its theoretical and empirical success, RankSEG relies on the restrictive Conditional Independence Assumption (CIA), which ignores crucial label correlations and therefore degrades performance in ambiguous or low-contrast scenarios. However, accounting for full label dependence is computationally prohibitive, requiring $\mathcal{O}(d^3)$ time. To address this, we replace the CIA with a Spatially Localized Dependence (SLD) structure that captures local label correlations while keeping the depend...
| Authors | Zixun Wang, Ben Dai |
|---|---|
| Primary category | cs.CV |
| Published | 30 September 2026 |
| Last updated at source | 12 October 2026 |
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