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Author (up) Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich edit  url
doi  openurl
  Title Multi-Modal Aerial View Image Challenge: Translation From Synthetic Aperture Radar to Electro-Optical Domain Results-PBVS 2023 Type Conference Article
  Year 2023 Publication Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal  
  Volume Issue Pages 515-523  
  Keywords  
  Abstract This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations.  
  Address Vancouver; Canada; June 2023  
  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 CVPRW  
  Notes MSIAU Approved no  
  Call Number Admin @ si @ LNS2023a Serial 3913  
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