解决分割模型置信度校准中的两个关键问题：不变性与决策保持。
Rethinking Post-Hoc Calibration in Semantic Segmentation
- 提出平移不变校准器，消除特征偏移对校准结果的干扰。
- 设计决策保持型校准方法，避免校准破坏分割结果质量。
- 在自然图像与医学图像上验证，兼具高置信度准确性和分割精度。
可靠的置信度估计对语义分割至关重要，尤其在安全关键场景中，过度自信的错误可能误导下游决策。然而现代分割模型常存在校准不足问题。后处理校准无需重训练即可修正置信度，但在密集预测任务中存在未被充分关注的结构性问题。我们研究了两个核心问题：首先，给所有logits加常数不会改变softmax概率，但部分标准校准器仍受此任意偏移影响，导致相同预测分布产生不同校准结果。为此，我们定义了平移不变（TI）校准器，并构造出具备该性质的校准器以消除表示依赖。其次，后处理校准通常基于似然目标优化，而分割模型训练使用Dice等任务特定指标，这种不匹配可能导致校准改变类别排序并损害分割图质量。我们研究了保持决策顺序的校准方法，包括argmax和排序不变约束。由于强制这些约束会将仿射softmax校准器退化为温度缩放，我们引入了类别条件仿射校准器，在保持决策不变的同时保留更强表达能力，从而量化决策保持带来的校准-分割权衡。在自然图像与医学分割基准以及基于扰动的协变量偏移下，对比实验表明，TI版本普遍提升校准性能，而决策保持版本可防止分割退化并维持良好校准效果。这些结果为语义分割中的可靠后处理校准提供了实用设计原则。
原文摘要 · Abstract (English)
Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead downstream decisions. Yet modern segmentation models often remain miscalibrated. Post-hoc calibration offers a practical way to correct confidence estimates without retraining the segmentation model, but its use in dense prediction raises structural issues that are often overlooked. We study two such issues. First, adding a constant to all logits leaves the softmax probabilities unchanged, but several standard calibrators can still depend on this arbitrary offset. As a result, two logit representations encoding the same predictive distribution may yield different calibrated probabilities. We define translation-invariant (TI) calibrators as those whose outputs are unchanged under such shifts, characterize which common calibrators satisfy this property, and construct TI counterparts of shift-sensitive calibrators to isolate the effect of removing representation dependence. Second, post-hoc calibration is typically fitted by minimizing a likelihood-based objective, whereas segmentation models are trained with task-specific metrics such as Dice. This mismatch can cause calibration to alter class orderings and degrade the deployed segmentation map. We study decision-preserving calibration under argmax- and order-preservation constraints. Since enforcing these constraints collapses affine softmax calibrators to temperature scaling, we introduce class-conditional affine calibrators that can be made argmax- or order-preserving while retaining greater expressivity, allowing us to quantify the calibration-segmentation trade-off induced by decision preservation. Across natural-image and medical segmentation benchmarks, and under corruption-based covariate shift, matched comparisons show that TI variants generally improve calibration metrics, while decision-preserving variants prevent segmentation degradation and retain strong calibration performance. These results provide practical design principles for well-defined post-hoc calibration pipelines in semantic segmentation.
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