CLIP(对比语言-图像预训练)是由OpenAI于2021年提出的多模态模型,通过对比学习实现图像与文本的跨模态语义对齐,成为计算机视觉与自然语言处理领域的里程碑。以下是其核心原理、技术特点及应用场景的详细解析:…
从中可以看出,CLIP 相较于 Visual N-Grams,取得了优异 Zero-shot 分类性能,这也表明 CLIP 较好地关联了图像和文本模态。 此外,论文对比了 CLIP 与ResNet50(在 ImageNet 上预训练,再加上线性分类器进行微调)在 27 个数据集上的性能。CLIP 在 16 个数据集上优于 ResNet50,但在卫星图像分类、淋巴结转移检测...
结合其他模态(如音频、视频)扩展CLIP的应用范围。 通过不断改进和完善,CLIP有望在未来成为多模态学习领域的核心工具之一。
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 ...
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...
(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 ...
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. ...
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...