作者证明了从头开始训练的 ConVIRT 的简化版本,称之为CLIP,用于对比语言-图像预训练(Contrastive Language-Image Pre-training),是一种从自然语言监督中学习的有效且可扩展的方法。作者发现 CLIP 在预训练期间学会了执行一系列广泛的任务,包括 OCR、地理定位和动作识别,并且优于公开可用的最佳 ImageNet 模型,同时计算效...
Learning Transferable Visual Models From Natural Language Supervision. 2021. ICML 1. 概述 1.1 解决问题 数据依赖:克服了传统视觉模型对大量标注数据的依赖。 通用性:提高了模型的通用性和可用性,使其能够在没有特定数据集训练的情况下执行多种任务。 1.2 创新点 多模态对比学习:使用对比学习方法,通过预测图像和...
Zeng, Yihan, et al. "CLIP2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. 作者单位:华为诺亚方舟实验室 香港科技大学 香港中文大学 中山大学 paper:https://openaccess.thecvf.co...
大量实验表明了我们的DeCLIP的有效性和效率。如图1所示,使用ResNet50图像编码器和Transformer文本编码器,我们的模型可以在ImageNet上实现60.4%的zero-shot top1精度,比CLIP ResNet50高0.8%,同时使用7.1×更少的数据。仅使用88M图像-文本对,我们最好的ResNet50/ViT B32模型将零拍性能提高到62.5%和66.2%,比最好的...
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CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities...
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a diff...
Recent advancements in Contrastive Language-Image Pre-training (CLIP)[ 21 ] have demonstrated notable success in self-supervised representation learning ac... Y Du,B Chang,NC Dvornek - International Conference on Medical Image Computing & Computer-assisted Intervention 被引量: 0发表: 2024年 Large ...
Recent advancements in Contrastive Language-Image Pre-training (CLIP)[ 21 ] have demonstrated notable success in self-supervised representation learning ac... Du, Yuexi,Chang, Brian,Dvornek, Nicha C. - International Conference on Medical Image Computing and Computer-Assisted Intervention 被引量: 0发...
尽管 Resnet101 在 Imagenet 上的准确度很高,但如果迁移到其他数据集上,它对于香蕉的识别准确度就大幅下降。但反观 CLIP,它可以识别动漫里的、表情包里的、素描画里的各种类型的香蕉,迁移能力很强。数据集 作者提到,现有的图像数据集都太小了。像 Imagenet 21k 才有 1400 万张图片。还有一些数据集虽然够大,...