Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of ...
3 Latent Language Image Pretraining 这段文字描述了我们提出的方法:潜在语言图像预训练(Latent Language Image Pretraining,简称Llip)。Llip学习输出一个基于文本标题的视觉表示。因此,在推理过程中,根据考虑的标题不同,图像会有不同的表示。我们的方法依赖于两个架构组件(如图2所示):一个输出K个视觉混合成分的视觉...
pre-training is an effective approach to improve model performance in low data regimes, in this paper, we find that existing pre-training methods are ill-suited for 3D scene graphs. To solve this issue, we present the first language-based pre-training approach for 3D scene graphs, whereby ...
Contrastive learning pre-trains an image encoder using a large amount ofunlabeled data such that the image encoder can be used as a general-purposefeature extractor for various downstream tasks. In this work, we proposePoisonedEncoder, a data poisoning attack to contrastive learning. Inparticular, ...