图像分类的应用场景非常广泛, 很多场景下难以收集到足够多的数据来训练模型,
利用小样本学习进行图像分类可解决训练数据量小的问题. 本文对近年来的小样本图像分类算法 …

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and
unseen classes, when only the labeled examples from seen classes are provided. Recent …

This paper focuses on the unsupervised domain adaptation of transferring the knowledge
from the source domain to the target domain in the context of semantic segmentation …

Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared
in the training data, has arisen hot research interests. The key of implementing ZSL is to …

Due to the lack of properly annotated medical data, exploring the generalization capability of
the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in …

Most zero-shot learning (ZSL) methods aim to learn a mapping from visual feature space to
semantic feature space or from both visual and semantic feature spaces to a common joint …

Generalized zero-shot learning (GZSL) aims to recognize samples from all classes based on
training samples of seen classes by bridging the gap between the seen and unseen classes …

Generative adversarial networks (GANs) for (generalized) zero-shot learning (ZSL) aim to
generate unseen image features when conditioned on unseen class embeddings, each of …

Image classification is widely used in different fields. However in many scenarios, it is
difficult to collect sufficient data to train the model for classification. Small sample learning …

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to semantically
related unseen classes, which are absent during training. The promising strategies for ZSL …