论文标题
关于属性的先验知识：学习零射击识别的更有效的潜在空间
Prior Knowledge about Attributes: Learning a More Effective Potential Space for Zero-Shot Recognition
论文作者
论文摘要
零拍学习（ZSL）的目的是通过学习看到的类和已知属性来准确地识别看不见的类，但是先前的研究忽略了属性的相关性，从而导致分类结果困惑。为了解决此问题，我们构建了一个使用图卷积网络和属性相关性来生成更具区别的潜在空间的属性相关性空间生成（ACPSG）模型。结合潜在的歧视空间和用户定义的属性空间，我们可以更好地对看不见的类进行分类。我们的方法的表现优于几个基准数据集上的一些现有最新方法，无论是常规的ZSL还是广义ZSL。
Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused. To solve this problem, we build an Attribute Correlation Potential Space Generation (ACPSG) model which uses a graph convolution network and attribute correlation to generate a more discriminating potential space. Combining potential discrimination space and user-defined attribute space, we can better classify unseen classes. Our approach outperforms some existing state-of-the-art methods on several benchmark datasets, whether it is conventional ZSL or generalized ZSL.
