Open AccessProceedings Article
Visual Entity Linking: A Preliminary Study
Rebecka Weegar,Linus Hammarlund,Agnes Tegen,Magnus Oskarsson +3 more
- 18 Jun 2014
- pp 46-49
TL;DR: A system that jointly extracts entities appearing in images and mentioned in their accompanying captions through a sequence of processing steps: partof-speech tagging, dependency parsing, and coreference resolution that enables it to identify the entities as well as possible textual relations from the captions.
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Abstract: In this paper, we describe a system that jointly extracts entities appearing in images and mentioned in their accompanying captions. As input, the entity linking program takes a segmented image together with its caption. It consists of a sequence of processing steps: partof-speech tagging, dependency parsing, and coreference resolution that enables us to identify the entities as well as possible textual relations from the captions. The program uses the image regions labelled with a set of predefined categories and computes WordNet similarities between these labels and the entity names. Finally, the program links the entities it detected across the text and the images. We applied our system on the Segmented and Annotated IAPR TC-12 dataset that we enriched with entity annotations and we obtained a correct assignment rate of 55.48%
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Citations
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
TL;DR: This work proposes a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding, and identifies the connotation of objects beyond their denotation.
Knowledge-rich image gist understanding beyond literal meaning
Lydia Weiland,Ioana Hulpus,Simone Paolo Ponzetto,Wolfgang Effelsberg,Laura Dietz +4 more
- 01 Sep 2018
TL;DR: In this paper, the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles is investigated, and a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge is proposed.
10
Understanding the Message of Images with Knowledge Base Traversals
Lydia Weiland,Ioana Hulpus,Simone Paolo Ponzetto,Laura Dietz +3 more
- 12 Sep 2016
TL;DR: A proposed algorithm that brings together aspects of entity linking, subgraph selection, entity clustering, relatedness measures, and learning-to-rank for understanding the message encoded by images and their captions to answer the question of how well algorithms can describe an image-caption pair in terms of Wikipedia entities.
7
Visual Named Entity Linking: A New Dataset and A Baseline
Wen Sun,Yixing Fan,Jiafeng Guo,Ruqing Zhang,Xueqi Cheng +4 more
- 01 Jan 2022
TL;DR: Visual Named Entity Linking (VEL) is a task to link regions of images with their corresponding entities in Knowledge Bases (KBs). VNEL is a purely Visual-based Named Entity Linking (VNEL) task where the input only consists of an image.
Linking Entities Across Images and Text
Rebecka Weegar,Kalle rAström,Pierre Nugues +2 more
- 01 Jul 2015
TL;DR: A set of methods to link entities across images and text to produce mappings in the form of pairs consisting of a region label and a caption entity, which could match objects in pictures to their correct mentions for nearly 89 percent of the segments, when such a matching exists.
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