Roei Herzig

Hi there! I'm Roei, a first-year Ph.D. student in Computer Science at Tel Aviv University, working with Prof. Gal Chechik, Prof. Amir Globerson and Prof. Trevor Darrell, and a member of the Berkeley AI Research Lab.

I'm also a Machine Learning & Deep Learning Researcher, I have worked at Nexar and Trax Image Recognition in the last 5 years. I graduated Magna Cum Laude from Tel Aviv University with MSc (CS), BSc (CS) and BSc (Physics).

fast-texture I'm looking for a strong MSc students that wish to collaborate and publish in top-tier conferences.

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Research

I mainly focus on machine learning models and deep learning methods for video and image understanding using structure (e.g. Structured Prediction). I believe our world is compositional and humans don't perceive the world as raw pixels. Moreover, structured models can enjoy the properties of generalization and inductive-bias, which I find critical, especially at the intersections of vision, language and robotics.

Research Interest:

  • Vision & Language: Object Detection, Scene Understanding, Visual Reasoning.
  • Vision & Robotics: Transfer Learning, Structured Representation, Semantic Understanding.
  • Machine Learning & Deep Learning: Semi-Supervised Learning, Self-Supervised Learning, Unsupervised Learning, Generative Models, Graph Neural Networks.

fast-texture Differentiable Scene Graphs fast-texture
Moshiko Raboh* , Roei Herzig*, Gal Chechik, Jonathan Berant, Amir Globerson
Winter Conference on Applications of Computer Vision (WACV) , 2020
bibtex

We propose an intermediate “graph-like” representation (DSGs) that can be learned in an end-to-end manner from the supervision for a downstream visual reasoning task, which achieves a new state-of-the-art results on Referring Relationships task.

fast-texture Spatio-Temporal Action Graph Networks fast-texture
Roei Herzig*, Elad Levi* , Huijuan Xu*, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson , Trevor Darrell
IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) , 2019  (Oral)
Code / bibtex

We propose a latent inter-object graph representation for activity recognition that explores the visual interaction between the objects in a self-supervised manner.

fast-texture Accurate Visual Localization for Automotive Applications
Eli Brosh*, Matan Friedmann*, Ilan Kadar*, Lev Yitzhak Lavy*, Elad Levi*, Shmuel Rippa*, Yair Lempert, Bruno Fernandez-Ruiz, Roei Herzig, Trevor Darrell
Workshop on Autonomous Driving at CVPR , 2019
blog / code / dataset / bibtex

We propose a hybrid coarse-to-fine approach that leverages visual and GPS location cues with on a new large-scale driving dataset based on video and GPS data.

fast-texture Precise Detection in Densely Packed Scenes
Eran Goldman*, Roei Herzig*, Aviv Eisenschtat* , Jacob Goldberger, Tal Hassner
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2019
code / dataset / bibtex

We collect a new SKU-110K dataset which takes detection challenges to unexplored territories, and propose a novel mechanism to learn deep overlap rates for each detection.

fast-texture Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Roei Herzig*, Moshiko Raboh* , Gal Chechik, Jonathan Berant, Amir Globerson
Advanced in Neural Information Processing Systems (NeurIPS) , 2018
code / bibtex

We propose a novel invariant graph network for mapping images to scene graphs using the permutation invariant property, which achieves a new state-of-the-art results on Visual Genome dataset.


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