论文标题
语义增强知识图，用于大规模零局学习
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning
论文作者
论文摘要
零击学习一直是视觉和语言领域的突出研究主题。最近，大多数现有方法采用结构化的知识信息来模拟类别之间的明确相关性，并使用深图卷积网络在不同类别之间传播信息。但是，很难在现有的结构化知识图中添加新类别，而深度图卷积网络遇到了过度平滑的问题。在本文中，我们提供了一个新的语义增强知识图，其中包含专家知识和类别语义相关性。我们的语义增强知识图可以进一步增强类别之间的相关性，并使吸收新类别的相关性。为了在知识图上传播信息，我们提出了一个新型的残留图卷积网络（RESGCN），可以有效地减轻过度光滑的问题。在广泛使用的大规模Imagenet-21K数据集和AWA2数据集上进行的实验显示了我们方法的有效性，并在零摄像学习上建立了新的最新技术。此外，我们在具有各种特征提取网络的大规模Imagenet-21K上的结果表明，我们的方法具有更好的概括和鲁棒性。
Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.
