发表机构
Imperial College London; The Chinese University of Hong Kong（帝国理工学院; 香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生物医学图像少样本分类中支持集组成导致的预测偏差，提出提示锚定残差自适应（PARA），以冻结提示预测为锚点学习残差，实现最先进的少样本分类与泛化性能。
AI 中文摘要
预训练的生物医学视觉语言模型在生物医学图像分类中展现出强大的零样本性能。然而，下游生物医学分类往往依赖于类别之间细微的视觉差异，这些差异可能未被预训练表示完全捕获。少样本自适应通过在小型标注支持集上优化任务特定的预测器来解决这一不匹配问题。由于所选示例仅捕获目标类别内部分视觉变化，自适应预测可能强烈依赖于其组成。我们提出提示锚定残差自适应（PARA），该方法保留冻结的提示预测作为支持不变的语义锚点，并通过锚点相对残差整合从支持集学习到的视觉预测。残差步骤以封闭形式从冻结的支持嵌入中计算，利用锚点差异和支持一致性。支持集依赖性也限制了评估：在同一抽取内的比较是公平的，但仍有条件于其组成。为获得更可靠的比较，我们引入重复支持协议，该协议将支持选择变化与优化随机性分离，并报告平均性能及最差20%性能。PARA在少样本分类和基础到新颖泛化方面均达到最先进性能。
英文摘要
Pretrained biomedical vision-language models achieve strong zero-shot performance in biomedical image classification. However, downstream biomedical classification often depends on subtle visual differences between classes that may not be fully captured by pretrained representations. Few-shot adaptation addresses this mismatch by optimizing a task-specific predictor on a small labeled support set. Because the selected examples capture only part of the visual variation within the target classes, the adapted predictions can depend strongly on their composition. We propose Prompt-Anchored Residual Adaptation (PARA), which retains the frozen prompt prediction as a support-invariant semantic anchor and incorporates a visual prediction learned from the support set through an anchor-relative residual. The residual step is computed in a closed form from frozen support embeddings using anchor discrepancy and support agreement. Support-set dependence also limits evaluation: comparisons are fair within a shared draw but remain conditional on its composition. To obtain more reliable comparisons, we introduce a repeated-support protocol that separates support-selection variation from optimization randomness and reports both average and worst-20% performance. PARA achieves state-of-the-art performance in both few-shot classification and base-to-novel generalization.
Comments18 pages, including appendix
