Jan 5, 2021 · CLIP (Contrastive Language–Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.

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 of GPT-2 and 3.

本文提出了一种简单而有效的基于多模态预训练模型 CLIP 的小样本微调算法—— cross-modal adaptation ，通过将跨模态信息（例如文字标签）作为训练样本加入交叉熵损失(Cross-Entropy Loss, CE Loss)进行微调，即可实现用一个简单的线性分类器在十一个图像识别训练集中 ...

Nov 30, 2024 · CLIP Adapter作为一种轻量级且高效的模型扩展方法，为CLIP模型的应用提供了更多的可能性。 通过引入适配层， CLIP Adapter 能够在保持 CLIP 模型 强大表征能力的同时，更好地适应下游任务，从而提升 模型 的性能。

Nov 7, 2024 · CLIP is a foundational multimodal model that aligns image and text features into a shared space using contrastive learning on large-scale image-text pairs. Its strength lies in leveraging natural language as a rich supervisory signal.

Feb 24, 2024 · CLIP was released by OpenAI in 2021 and has become one of the building blocks in many multimodal AI systems that have been developed since then. This article is a deep dive of what it is, how...

关键词：clip、多模态. 代码： https:// github.com/OpenAI/CLIP. 一句话总结：利用text信息监督视觉任务自训练，本质就是将分类任务化成了图文匹配任务，效果可与全监督方法相当 ； 0. Abstract. 目前sota的视觉系统局限性在哪里？

Dec 11, 2023 · CLIP, which stands for Contrastive Language-Image Pretraining, is a deep learning model developed by OpenAI in 2021. CLIP’s embeddings for images and text share the...

Feb 19, 2025 · 训练. CLIP包含两个核心模型，分别是文本编码器（Text Encoder）和图像编码器（Image Encoder）。其中，文本编码器的作用是提取文本的特征，在实现时可采用自然语言处理（NLP）领域常用的文本Transformer模型；而图像编码器则用于提取图像的特征，在实际应用中可以选用常见的卷积神经网络（CNN）模型 ...

OpenAI Contrastive Learning In Pretraining (CLIP) is a world scope three model. It can comprehend concepts in both text and image and even connect concepts between the two modalities. In this chapter we will learn about multi-modality, how CLIP works, and how to use CLIP for different use cases like encoding, classification, and object detection.