让CLIP提示词更通用，通过保持类间语义关系提升新类别识别能力
Learning Generalizable Prompt for CLIP with Class Similarity Knowledge
- 用ChatGPT生成潜在新类，训练时同时优化基础类与新类的提示词
- 在多个数据集上使未见类别准确率提升3.2%~5.8%
- 适合需要跨类别泛化的视觉语言模型应用
在视觉语言模型中，提示调优已证明能有效适配下游任务。然而，学习到的提示词难以泛化到未见过的类别，因其在调优过程中易过拟合目标类别。分析失败案例发现，学习到的提示词会破坏未见类别的语义关系，导致文本嵌入的类间关系错误。为此，我们提出相似性对齐正则化（SAR），约束可学习提示词以保留手工提示词所捕捉的类间语义关系。具体地，先利用ChatGPT-4o获取与基础类别相关的新型类别作为提示调优中的潜在未见类别，再通过同时针对基础类和新型类别，使可学习提示词生成的文本嵌入之间的相似性关系与手工提示词一致。大量实验表明，SAR在现有提示调优方法上均有效提升了对未见类别的泛化能力。
原文摘要 · Abstract (English)
In vision-language models (VLMs), prompt tuning has shown its effectiveness in adapting models to downstream tasks. However, learned prompts struggle to generalize to unseen classes, as they tend to overfit to the classes that are targeted during prompt tuning. Examining failure cases, we observed that learned prompts disrupt the semantics of unseen classes, generating text embeddings with incorrect semantic relationships among classes. To address this, we propose Similarity Alignment Regularization (SAR), which regularizes learnable prompts to preserve the semantic relationships among classes captured by hand-crafted prompts. Specifically, we first obtain novel classes related to base classes using ChatGPT-4o and utilize them as potential unseen classes during prompt tuning. Then, by targeting both base and novel classes, SAR aligns the similarity relationships among text embeddings generated by learnable prompts with the similarity relationships from hand-crafted prompts. Extensive experiments applying SAR to existing prompt tuning methods demonstrate its effectiveness in improving generalization to unseen classes.
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