SOURCE-LINKED INTELLIGENCE

Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

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-Balanc

Read original source ↗ Open in workspace

recordType

paper

region

Global