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Visual Entity Linking (VEL) is a task to link regions of images with theircorresponding entities in Knowledge Bases (KBs), which is beneficial for manycomputer vision tasks such as image retrieval, image caption, and visualquestion answering. While existing tasks in VEL either rely on textual data tocomplement a multi-modal linking or only link objects with general entities,which fails to perform named entity linking on large amounts of image data. Inthis paper, we consider a purely Visual-based Named Entity Linking (VNEL) task,where the input only consists of an image. The task is to identify objects ofinterest (i.e., visual entity mentions) in images and link them tocorresponding named entities in KBs. Since each entity often contains richvisual and textual information in KBs, we thus propose three differentsub-tasks, i.e., visual to visual entity linking (V2VEL), visual to textualentity linking (V2TEL), and visual to visual-textual entity linking (V2VTEL).In addition, we present a high-quality human-annotated visual person linkingdataset, named WIKIPerson. Based on WIKIPerson, we establish a series ofbaseline algorithms for the solution of each sub-task, and conduct experimentsto verify the quality of proposed datasets and the effectiveness of baselinemethods. We envision this work to be helpful for soliciting more worksregarding VNEL in the future. The codes and datasets are publicly available athttps://github.com/ict-bigdatalab/VNEL.

Visual Named Entity Linking: A New Dataset and A Baseline