Elias Ramzi - Deep learning PhD student

I am a PhD student in deep learning at Consevatoire national des arts et métiers, Le Cnam, in Paris. I am supervised by Nicolas Thome (Sorbonne Université), Nicolas Audebert (Le Cnam) and Clément Rambour (Le Cnam). My thesis is co-financed by Coexya and my industrial supervisor is Xavier Bitot. I investigate deep learning approaches to image retrieval. Specifically, I work on designing appropriate losses to train deep neural networks to optimize ranking losses. I also contributed on collaborative filtering recommendation using graph neural networks and on OOD detection using energy-based models. I have started my PhD in January 2021 and it will end in January 2024. I have published three projects, ROADMAP at NeurIPS 2021, HAPPIER at ECCV 2022 and HEAT at ICML 2023. I have submitted an extension of ROADMAP and HAPPIER to TPAMI, which is under-review.


  • My pre-print submitted to TPAMI is now online at https://arxiv.org/abs/2309.08250.
  • I am going to ICML 2023. I will be presenting HEAT in a poster session.
  • I am participating to the Internation Computer Vision Summer School (ICVSS, 2023) this summer in Sicily.
  • I have released the first of its kind hierarchical landmark retrieval dataset as a part of a TPAMI submission. It is available at https://github.com/cvdfoundation/google-landmark.
  • I am a reviewer for NeurIPS 2023.
  • I am going to ORASIS 2023. I will be presenting HAPPIER in a poster session.
  • Our paper on OOD detection, HEAT, using energy-based models has been accepted to ICML 2023.
  • I am a reviewer for ICML 2023.
  • I served as a sub-reviewer for CVPR 2023.
  • I am going to present our ECCV 2022 paper, HAPPIER, to the 25th of October in Tel Aviv.
  • Our paper on hierarchical image retrieval, HAPPIER, has been accepted to ECCV 2022.
  • I will be presenting our ROADMAP paper at RFIAP 2022.
  • Our paper on ranking metric optimization for image retrieval, ROADMAP, has been accepted to NeurIPS 2021.