论文标题
视觉语义嵌入模型以结构化知识告知
Visual-Semantic Embedding Model Informed by Structured Knowledge
论文作者
论文摘要
我们提出了一种新颖的方法，可以通过合并从外部结构化知识库中捕获的概念表示来改善视觉语义嵌入模型。我们研究了其在标准和零摄影设置下的图像分类方面的性能。我们提出了两个新颖的评估框架，以分析知识库指示的类层次结构的分类错误。使用ILSVRC 2012图像数据集和WordNet知识库测试该方法。关于标准图像分类，我们的方法与使用单词嵌入的原始方法相比显示出优异的性能。
We propose a novel approach to improve a visual-semantic embedding model by incorporating concept representations captured from an external structured knowledge base. We investigate its performance on image classification under both standard and zero-shot settings. We propose two novel evaluation frameworks to analyse classification errors with respect to the class hierarchy indicated by the knowledge base. The approach is tested using the ILSVRC 2012 image dataset and a WordNet knowledge base. With respect to both standard and zero-shot image classification, our approach shows superior performance compared with the original approach, which uses word embeddings.
