Web Reference: 简单的说,CLIP 无需利用 ImageNet 的数据和标签进行训练,就可以达到 ResNet50 在 ImageNet数据集上有监督训练的结果,所以叫做 Zero-shot。 CLIP(contrastive language-image pre-training)主要的贡献就是 利用无监督的文本信息,作为监督信号来学习视觉特征。 CLIP (Contrastive Language-Image Pre-Training) 模型 是 OpenAI 在 2021 年初发布的用于 匹配图像和文本 的 预训练 神经网络模型,是近年来多模态研究领域的经典之作。该模型直接使用 大量的互联网数据 进行预训练,在很多任务表现上达到了SOTA 。 从检索这个角度来看,CLIP的zero shot其实就是把分类问题转化为了检索问题。 总结来看,CLIP能够zero shot识别,而且效果不错的原因在于: 1、训练集够大,zero shot任务的图像分布在训练集中有类似的,zero shot任务的concept在训练集中有相近的;
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