Vicky Kalogeiton
Scholar

Vicky Kalogeiton

Google Scholar ID: gIRvhKkAAAAJ
École Polytechnique, IP Paris
Computer VisionDeep Learning
Citations & Impact
All-time
Citations
1,634
 
H-index
17
 
i10-index
25
 
Publications
20
 
Co-authors
14
list available
Resume (English only)
Academic Achievements
  • Published papers in top computer vision conferences (CVPR, ICCV, ECCV) and journals (T-PAMI, IJCV).
  • Awards:
  • - Best Student Honorable Mention Award at ACCV 2022
  • - Best Paper Award at CVEU ICCV-W 2021
  • - DIM RFSI 2021 grant
  • - Top 215 Reviewers in ECCV’20
  • - Outstanding Area Chair for ACCV 2022
  • - Outstanding Reviewer for ICCV 2021
  • - Program Chair for CVPR 2027
  • - Diversity Chair & Area Chair for ICCV 2025
  • - Hi!Paris chaire
  • - Area Chair for WACV'24, ACCV'24, and ECCV'24
  • - Hi!Paris grant
  • - Microsoft Academic gift
  • - Funding for two projects: JCJC WhyBehindScenes and ANR APATE
  • - Co-organized the Doctoral Symposium of AIML Systems 2021
  • - Co-organized the 1st Workshop on Future Video Conferencing (FVC) at CVPR 2021
  • - Co-organized the Real-World Computer Vision from Inputs with Limited Quality Workshop (RLQ) at ICCV 2021
  • - Area Chair for CVPR 2021
Research Experience
  • Current: Professor at the Computer Science Laboratory (LIX) of École Polytechnique, head of the VISTA team.
  • Former: Research Fellow at VGG, University of Oxford.
Education
  • PhD: CALVIN group, University of Edinburgh; THOTH team, INRIA Grenoble (previously LEAR), advised by Vittorio Ferrari and Cordelia Schmid.
  • Postdoctoral Research Fellow: VGG, University of Oxford, working with Andrew Zisserman.
Background
  • Professor (HDR, 2024 from Polytechnique) in AI at the Computer Science Laboratory (LIX) of École Polytechnique, Paris, France. She is the head of the VISTA team and an Ellis member attached to the Paris Unit. Her research goal is to develop generalizable methods applicable to various domains, and her current focus is on multimodal generative AI, particularly in terms of efficiency, structured or multiple outputs, and medical applications.
Miscellany
  • Supports Slow Science and Open Science.