pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(Audio)、计算机视觉 今天介绍CV计算机视觉的第七篇,零样本图像分类(zero-shot-image-classification),在huggingface库内有500个零样本图像分类模型。 二、零样本图像分类(zero-shot-image-classification) 2.1 概述 零...
使用CLIP模型可以很方便地实现零样本图片分类（Zero Shot Image Classification），广泛效果好，且图片类别...
1 任务说明 现有的benchmark通过ImageNet-1k上预训练的Res101从已知类的训练集提取feature或者feature map,然后对每一个类引入一个语义标签,可能是属性标签(attribute label)、或者描述标签(sentence embedding)等。对于某个类的属性标签(向量形式),每个维度表示一种属性,该维度下的取值表示这个属性在该类别中存在的可...
Few-shot Learning V.S Zero-shot Learning 小样本学习的目的是在有少量训练数据的情况下能获得准确分类测试样本的模型 零样本学习的目的是预测训练数据集中没有出现过的类 零样本学习和小样本学习有很多共同的应用,如: 图像分类 (image classification) 语义分割 (semantic segmentation) 图像生成 (image generation)...
In addition, zero-shot classification also faces domain drift problems caused by non-intersecting training and testing categories. Therefore, this paper proposes a zero-shot image classification method based on decoupling of visual鈥搒emantic features, which alleviates modal heterogeneity and domain drift...
Li J, Savarese S, Hoi S C H. Masked Unsupervised Self-training for Zero-shot Image Classification[J]. arXiv preprint arXiv:2206.02967, 2022. 摘要导读 有监督学习由于较为昂贵的标注费用会限制模型的可扩展性。虽然自监督表示学习已经取得了令人印象深刻的进展,但它仍然需要对标记数据进行第二阶段的微调...
Background: At present, two semantic vectors, word embedding vector and attribute vector, have been often used for category representation in the zero-shot image classification process. However, these two representation forms of the semantic vector suffer from two problems. The first problem is that...
To this end, Zero-Shot Image Classification (ZIC) is proposed, which aims to make machines that can learn to classify unseen images like humans. The problem can be viewed from two different levels. Low-level technical issues are concerned by the general Zero-shot Learning (ZSL) problem which...
Few-shot Learning V.S Zero-shot Learning 小样本学习的目的是在有少量训练数据的情况下能获得准确分类测试样本的模型 零样本学习的目的是预测训练数据集中没有出现过的类 零样本学习和小样本学习有很多共同的应用,如: 图像分类 (image classification)
Few-shot Learning V.S Zero-shot Learning 小样本学习的目的是在有少量训练数据的情况下能获得准确分类测试样本的模型 零样本学习的目的是预测训练数据集中没有出现过的类 零样本学习和小样本学习有很多共同的应用,如: 图像分类 (image classification)