CLIP的视觉嵌入投影器是少样本宝库
CLIP's Visual Embedding Projector is a Few-shot Cornucopia
AI总结：
ProLIP通过正则化投影矩阵提升CLIP在少样本分类及跨领域迁移中的性能，提供更高效的替代方案。
摘要：
我们介绍了一个简单且架构无关的方法ProLIP，用于将对比预训练的视觉-语言模型（如CLIP）适应到少样本分类任务中。ProLIP通过在投影矩阵偏离预训练权重时使用Frobenius范数正则化来微调视觉编码器的投影矩阵。在
英文摘要：
We introduce ProLIP, a simple and architecture-agnostic method for adapting contrastively pretrained vision-language models, such as CLIP, to few-shot classification. ProLIP fine-tunes the vision encoder's projection matrix with Frobenius norm regularization on its deviation from the pretrained weights. It achieves state-of-the-art performance on 11 few-shot classification benchmarks under both ``few-shot validation'' and ``validation-free'' settings. Moreover, by rethinking the non-linear CLIP-Adapter through ProLIP's lens, we design a Regularized Linear Adapter (RLA) that performs better, requires no hyperparameter tuning, is less sensitive to learning rate values, and offers an alternative to ProLIP in black-box scenarios where model weights are inaccessible. Beyond few-shot classification, ProLIP excels in cross-dataset transfer, domain generalization, base-to-new class generalization, and test-time adaptation--where it outperforms prompt tuning while being an order of magnitude faster to train. Code is available at https://github.com/astra-vision/ProLIP .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
