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...
CLIP 是 OpenAI 提出的多模态模型,它通过对比学习方法,不直接学习图像到类别的映射,而是学习图像语义和文本语义之间的匹配关系,将图像和文本共同映射到一个共享的语义空间中,为解决经典分类模型的问题提供了一种全新的思路。 clip原理图 以下是 CLIP 的核心概念和对经典问题的解释: 经典分类模型的问题与局限性 类别固...
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...
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...
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 ...
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...
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 images. Key Aspects of CLIP: Cross...
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...
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...