Visual Named Entity Linking: A New Dataset and A Baseline
Wenxiang Sun, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

TL;DR
This paper introduces a new dataset and baseline methods for purely visual named entity linking in images, focusing on linking image regions to entities in knowledge bases without relying on textual data.
Contribution
It proposes a new VNEL task with three sub-tasks, introduces the WIKIPerson dataset, and establishes baseline algorithms for visual entity linking.
Findings
High-quality human-annotated dataset created
Baseline algorithms demonstrate effectiveness
Experiments verify dataset and method quality
Abstract
Visual Entity Linking (VEL) is a task to link regions of images with their corresponding entities in Knowledge Bases (KBs), which is beneficial for many computer vision tasks such as image retrieval, image caption, and visual question answering. While existing tasks in VEL either rely on textual data to complement a multi-modal linking or only link objects with general entities, which fails to perform named entity linking on large amounts of image data. In this 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 of interest (i.e., visual entity mentions) in images and link them to corresponding named entities in KBs. Since each entity often contains rich visual and textual information in KBs, we thus propose three different sub-tasks, i.e., visual to visual entity linking (V2VEL), visual to…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning · Topic Modeling
