toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author (up) Ali Furkan Biten; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
url  doi
  Title Good News, Everyone! Context driven entity-aware captioning for news images Type Conference Article
  Year 2019 Publication 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 12458-12467  
  Abstract Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world. In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image. Our model is able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source. Furthermore we introduce“ GoodNews”, the largest news image captioning dataset in the literature and demonstrate state-of-the-art results.  
  Address Long beach; California; USA; june 2019  
  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 CVPR  
  Notes DAG; 600.129; 600.135; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ BGR2019 Serial 3289  
Permanent link to this record
Select All    Deselect All
 |   | 

Save Citations:
Export Records: