Learning Transferable Visual Models From Natural Language Supervision。它是一个 zero-shot 的视觉分类模型,预训练的模型在没有微调的情况下在下游任务上取得了很好的迁移效果。作者在30多个数据集上做了测试,涵盖了 OCR、视频中的动作检测、坐标定位等任务。CLIP 的效果没有在 ImageNet 上做微调的CLIP,就能和已经...
训练时使用ANN搜索得到的topk的image embedding 和关联的text embedding,使用multi head cross attention之后的特征增强目标模型的vision embedding,作为clip训练的vision侧特征,得到在下游任务上更强的效果 整体设计如图2 RAM是个6层的multi-head cross-attention,nothing fancy 用pretrain模型提取特征 ekI=ϕ(rkI)ekT=...
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...
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 instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, simila...
The idea is to learn more about the image using supervision from Natural Language Processing. However, its hard to find high quality large datasets that have crowd-labeled images with text. The paper introduces a new dataset of 400 million image x text pairs collected from internet...
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, Nengh...
self-supervised models such asContrastive Language-Image Pre-training(CLIP)17is particularly interesting both from a theoretical and a practical point of view. Building upon large pre-trained models to learngeneralconcepts in specific verticals/industries (e.g., Fashion, Electronics, DIY, etc.) may...
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...