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...
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...
(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 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 ...
Contrastive Language–Image Pre-training 简介 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 +...
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%,比最好的高出近3.0%这两种架构的报告数量。我们进一步...
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...
In a nutshell, CLIP is a multimodal model that combines knowledge of English-language concepts with semantic knowledge of images. From theOpenAI CLIP repository, "CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructe...
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks. However, due to the limited Text-3D data pairs, adapting the success of 2D Vision-Language Models (VLM) to the 3D space rema...