基于双层级置信度的医学视觉问答隐式自精炼
Dual-Level Confidence based Implicit Self-Refinement for Medical Visual Question Answering
提出双层级置信度（损失级与特征级）自适应融合为样本级权重，用于伪监督自训练，可迁移到其他视觉语言模型在分布偏移下的测试时自适应或半监督微调。
原文依据 we propose a dual-level confidence based framework (DuCoR) that achieves implicit self-refinement through iterative pseudo-supervised optimization.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论医学视觉问答模型常面临潜在的训练-测试分布偏移，阻碍其在未见过的成像和语言模式上的泛化。为解决这一挑战，我们提出了一个基于双层级置信度的框架（DuCoR），通过迭代伪监督优化实现隐式自精炼。该模型不依赖固定的伪答案，而是从两个互补的角度估计预测的可靠性来逐步精炼预测。损失级置信度通过建模干净和噪声损失分布来捕捉监督的可靠性，而特征级置信度则衡量样本表示与其伪答案条件原型之间的语义一致性。由于这两种置信度源自不同的信息源（包括监督信号和输入语义），它们提供相互纠正的线索。它们被自适应融合以得出每个样本的可靠性权重，引导伪监督优化更好地对齐目标分布。在多个医学视觉问答基准上的大量实验表明，我们的方法取得了优越的性能，并且与全监督基线相比展现出改进的跨域泛化能力。
英文原文摘要 1281 字符
Medical visual question answering models often face potential train-test distribution shifts that hinder generalization across unseen imaging and linguistic patterns. To address this challenge, we propose a dual-level confidence based framework (DuCoR) that achieves implicit self-refinement through iterative pseudo-supervised optimization. Instead of relying on fixed pseudo answers, the model progressively refines its predictions by estimating their reliability from two complementary perspectives. A loss-level confidence captures the reliability of supervision by modeling clean and noisy loss distributions, while a feature-level confidence measures the semantic coherence between sample representations and their pseudo-answer conditioned prototypes. Since these two confidences originate from distinct information sources, including the supervision signal and the input semantics, they provide mutually corrective cues. They are adaptively fused to derive per-sample reliability weights that guide pseudo-supervised optimization toward better alignment with the target distribution. Extensive experiments on multiple medical VQA benchmarks show that our method achieves superior performance and exhibits improved cross-domain generalization over fully supervised baseline.