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Author (up) Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
  Title Exploring Hate Speech Detection in Multimodal Publications Type Conference Article
  Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Abstract In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.  
  Address Aspen; March 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GGG2020a Serial 3280  
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