Visual Entity Linking

2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2017)

引用 8|浏览35
暂无评分
摘要
Entity linking is the task of identifying entities like people and places in textual data and linking them to corresponding entities in a knowledge base. In this paper we solve a visual equivalent of this task called visual entity linking. The goal is to link regions of images to corresponding entities in knowledge bases. Visual entity linking will enable computers to better understand visual content and thus can be used in tasks like image retrieval and visual question answering.More specifically, we propose a novel approach for linking image regions to entities in Dbpedia and Freebase. First, we select candidate entities using an automatic image description generation algorithm. We then extract image regions using object detection methods and compare them to depictions of entities in a knowledge base. We evaluate our approach on the Flickr8k dataset through surveys on Amazon Mechanical Turk, and present an extensive analysis to identify the sources of errors in our system.
更多
查看译文
关键词
visual entity linking,entity identification,image link regions,visual content,Dbpedia,Freebase,automatic image description generation algorithm,image region extraction,object detection methods,Amazon Mechanical Turk,Flickr8k dataset
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要