Learning Transferable Visual Models From Natural Language Supervision。它是一个 zero-shot 的视觉分类模型,预训练的模型在没有微调的情况下在下游任务上取得了很好的迁移效果。作者在30多个数据集上做了测试,涵盖了 OCR、视频中的动作检测、坐标定位等任务。CLIP 的效果没有在 ImageNet 上做微调的CLIP,就能和已经...
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...
contrastive languageimage pre-training Contrastive Language-Image Pre-training (CLIP) is a significant advancement in the field of artificial intelligence, particularly in the area of multimodal learning, where models learn to understand and relate information across different modalities, such as text and...
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...
(image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. For pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings ...
CLIP(Contrastive Language-Image Pretraining)是一种深度学习模型,它结合了语言和图像信息,通过对比学习的方式进行预训练。这种模型的目标是学习图像和文本之间的内在联系,以便能够理解和生成各种语言的文本描述。CLIP主要通过对比语言和图像的表示学习来实现其目标。具体来说,CLIP包含两个主要部分:文本编码器和图像编码器...
内容提示: PMC-CLIP: Contrastive Language-ImagePre-training using Biomedical DocumentsWeixiong Lin 1,∗ , Ziheng Zhao 1,∗ , Xiaoman Zhang 1,2 , Chaoyi Wu 1,2 , YaZhang 1,2 , Yanfeng Wang 1,2 , and Weidi Xie 1,2,†1Cooperative Medianet Innovation Center, Shanghai Jiao Tong ...
CLIPintroduces a model that enables zero shot learning for a new dataset (in addition to a new example) by using NLP to supervise pre-training. i.e., To identify an object, you can provide the name or description of a new object that the model has not seen before. ...
SUPERVISION EXISTS EVERYWHERE: A DATA EFFICIENT CONTRASTIVE LANGUAGE-IMAGE PRE-TRAINING PARADIGM 近年来,大规模对比语言图像预训练(CLIP)因其令人印象深刻的zero-shot识别能力和良好的下游任务转移能力而引起了前所未有的关注。然而,CLIP非常需要数据,需要400M图像-文本对进行预训练。这项工作提出了一种新的训练范式...
Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. Recently, CLIP has been widely adopted as the vision backbone of multimodal large language models (MLLMs) to connect image ...