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...
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 ...
Vision Pretraining:在大规模图像数据集上与训练一个ConvNet或者Transformer,用于解决下游的一些视觉任务,例如分类,定位,分割,识别,追踪等。 Vision-Language Pretraining:致力于使用一个融合模型来对视觉和语言进行联合编码。 Image-Text Foundation Models:包含Vision Pretraining和Vision-Language Pretraining。一个Image-...
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, ...
GPT模型是出自论文《Improving Language Understanding by Generative Pre-Training》,是自然语言处理中的预训练模型,是现在的GPT-3和GPT-2的基础。 GPT模型是基于Transformer上进行改进的,采用的是两阶段的方法,即先使用大量的语料库进行预训练,然后再针对对应的任务进行微调。(两阶段模式被证明是有效的,目前很多自然语...
The finding that pre-training a network on a rich source set (e.g., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little is known about its usefulness in 3D point ...
For example, when pre-training a model to do image classification, the induced features transfer reasonably well to other image classification domains, but also lack certain information such as color or the ability to count that are irrelevant for classification but relevant for e.g. image ...
* Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training* 链接: arxiv.org/abs/2201.0402* 作者: Yehao Li,Jiahao Fan,Yingwei Pan,Ting Yao,Weiyao Lin,Tao Mei* 其他: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)* 摘要: Vision...