Evgenii Zheltonozhskii

Technion – Israel Institute of Technology.

Vista lab, Taub Building

Technion

Haifa, 3200003

I’m a CS M.Sc. student in Technion, advised by Alex Bronstein and Avi Mendelson. You can find more details about my experience in resume.

Research interests: Self-supervised deep learning, applications of DL in physics, complex computer vision problems (e.g., panoptic segmentation, action segmentation), deep learning for 3D, Bayesian DL, bias in ML systems. I also have a Telegram channel where I post links to research I find interesting.

Previously: I was a research intern in Creative Vision team in Snap Research in Summer 2020. Before starting my Master’s, I was part of Rothschild Technion Program for Excellence and received double B.Sc. (CS and Physics+Math) from Technion.

news

Oct 4, 2021 Our paper “Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels” (C2D) got accepted to WACV 2022.
Aug 26, 2021 Our paper “CAT: Compression-Aware Training for Bandwidth Reduction” got accepted to JMLR.
Jan 10, 2021 Our paper “Early-stage neural network hardware performance analysis” got accepted to Special Issue “Energy-Efficient Computing Systems for Deep Learning” of Sustainability journal.
Oct 31, 2020 Our paper “Self-Supervised Learning for Large-Scale Unsupervised Image Clustering” got accepted to Self-Supervised Learning - Theory and Practice workshop at NeurIPS 2020.

selected publications

  1. arXiv
    End-to-End Referring Video Object Segmentation with Multimodal Transformers
    Adam Botach, Evgenii Zheltonozhskii, and Chaim Baskin
    arXiv pre-print 2021
  2. arXiv
    Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
    Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, and Or Litany
    arXiv pre-print 2021
  3. NeurIPS
    workshop
    Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
    Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, and Avi Mendelson
    NeurIPS Self-Supervised Learning Workshop 2020