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Author (up) Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas edit   pdf
  Title Location Sensitive Image Retrieval and Tagging Type Conference Article
  Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Abstract People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.  
  Address Virtual; August 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 ECCV  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GGG2020b Serial 3420  
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