新方法解决不平衡二值分割中熵最小化导致的预测坍塌问题
Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation
这是篇关于如何解决AI分割任务中一个具体问题的论文，方法很新颖，对做相关研究或应用的人可能有参考价值。
论文提出Balanced-Anchor Prompt Adaptation (BAPA)方法，针对测试时适应（TTA）中熵最小化标准在密集分割任务上的失效问题。该方法通过Class-Balanced Anchors (CBA)模块分别从每个预测类别中选择高置信度锚点，并赋予前景和背景相等的总损失权重，防止多数类主导更新。动态Prompt Adaptation (DPA)模块在每次预测更新后刷新这些锚点，仅优化文本提示残差而不改变预训练视觉表示。在四个领域的实验中，BAPA在评估方法中实现了最高的平均Dice分数。
Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation
Entropy minimization is a standard objective for test-time adaptation (TTA), but it can fail in imbalanced binary segmentation. Unlike image classification, dense segmentation aggregates thousands of pixel predictions, allowing the larger predicted class to dominate the update, pull minority predictions toward itself, and produce a degenerate mask as predictions saturate and their entropy gradients vanish. We theoretically establish this collapse in a shared-shift model. This analysis motivates Balanced-Anchor Prompt Adaptation (BAPA), which combines two complementary modules. The Class-Balanced Anchors (CBA) module selects high-confidence anchors separately from each predicted class and gives foreground and background equal total loss weight, preventing the larger region from dominating the update. Dynamic Prompt Adaptation (DPA) refreshes these anchors after each prediction update and optimizes only text-side prompt residuals while keeping the vision-language encoders frozen. This prompt-only update refines the foreground-background decision boundary without altering the pretrained dense visual representation. Across experiments from four domains, BAPA achieves the highest mean Dice among the evaluated methods. Factorized ablations further validate the complementary roles of CBA and DPA, supporting balanced prompt adaptation as an effective alternative to entropy minimization for test-time binary segmentation.