Contrastive Language-Image Pre-training(CLIP)[1] 技术由 OpenAI 团队在 ICML 2021 提出,这是一个非常符合 Open AI 大力出奇迹的工作风格。根据谢赛宁教授在智源大会上的分享 [2],目前大多数多模态大模型都采用了 CLIP 预训练的视觉编码器,足见 CLIP 的广泛影响力。本篇博文对 CLIP 的核心技术原理进行梳理和...
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...
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 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...
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 ...
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining Xiaoyi Dong1*†, Jianmin Bao2∗, Yinglin Zheng3, Ting Zhang2, Dongdong Chen4,†, Hao Yang2, Ming Zeng3, Weiming Zhang1, Lu Yuan4, Dong Chen2, Fang Wen2, Nengha...
CLIP(Contrastive Language-Image Pre-training) CLIP(Contrastive Language-Image Pre-training)是一种由OpenAI开发的多模态模型,用于处理图像和文本之间的关系。它能够在同一个模型中处理图像和文本的输入,而无需额外的调整或模型扩展。以下是对CLIP模型的原理和简单的代码实现解释:...
CLIP全称是Contrastive Language–Image Pre-training,是由OpenAI提出的一个具有里程碑式的多模态学习的transformer模型。此前的多模态学习基本是类似语音识别这种比较简单的多模态转换学习。而CLIP则是将视觉transformer(Vision Transformer)与文本处理相结合,采用text encoder + image encoder结合的方式进行文本-图像对的训练...
Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of ...
(image-text) contrastive loss and intra-domain (image-image) contrastive loss are defined on individual spaces in those works, so many feasible combinations of supervision are overlooked. To overcome this issue, we propose UniCLIP, a Unified framework for Contrastive Language-Image Pre-training. ...