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).

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

Email  /  Twitter  /  Github  /  LinkedIn  /  Google Scholar

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.

Differentiable Scene Graphs

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.

Spatio-Temporal Action Graph Networks

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

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.

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.

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.