Orr Zohar

Ph.D. Student, Stanford University

orrzohar [AT] stanford.edu

Bio

I am a PhD student in Computer Vision & Machine Learning in MARVL at Stanford University, where I am fortunate to be advised by Prof. Serena Yeung and funded by a Knight-Hennessy Scholarship.

My research focuses on Large Multi-Modal Models, especially Large Image/Video + Langauge models, with the hope of pushing these models to be capable of evaluating the quality of actions in video.

News

Publications

Most recent publications on Google Scholar.
indicates equal contribution.

Video-STaR: Bootstrapping Weak Video Supervision for Visual Instruction Tuning

Orr Zohar, Xiaohan Wang, Yonatan Bitton, Idan Szpektor, Serena Yeung-Levy

arXiv preprint (2024)

project arxiv code

VideoAgent: Long-form Video Understanding with Large Language Model as Agent

Xiaohan Wang*, Yuhui Zhang*, Orr Zohar, Serena Yeung-Levy

arXiv preprint (2024)

project arxiv code

Open World Object Detection in the Era of Foundation Models

Orr Zohar, Alejandro Lozano, Shelly Goel, Serena Yeung-Levy, Kuan-Chieh Wang

arXiv preprint (2023)

project arxiv code

LOVM: Language-Only Vision Model Selection

Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, Serena Yeung-Levy

In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (neurIPS 2023)

project proceeding arxiv code

PROB: Probabilistic Objectness for Open World Object Detection

Orr Zohar, Kuan-Chieh Wang, Serena Yeung-Levy

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)

project proceeding arxiv code

Video-STaR: Bootstrapping Weak Video Supervision for Visual Instruction Tuning

Orr Zohar, Xiaohan Wang, Yonatan Bitton, Idan Szpektor, Serena Yeung-Levy

arXiv preprint (2024)

project arxiv code

VideoAgent: Long-form Video Understanding with Large Language Model as Agent

Xiaohan Wang*, Yuhui Zhang*, Orr Zohar, Serena Yeung-Levy

arXiv preprint (2024)

project arxiv code

Open World Object Detection in the Era of Foundation Models

Orr Zohar, Alejandro Lozano, Shelly Goel, Serena Yeung-Levy, Kuan-Chieh Wang

arXiv preprint (2023)

project arxiv code

LOVM: Language-Only Vision Model Selection

Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, Serena Yeung-Levy

In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (neurIPS 2023)

project proceeding arxiv code

PROB: Probabilistic Objectness for Open World Object Detection

Orr Zohar, Kuan-Chieh Wang, Serena Yeung-Levy

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)

project proceeding arxiv code

Analyzing surgical technique in diverse open surgical videos with multitask machine learning

Emmett D Goodman, Krishna K Patel, Yilun Zhang, William Locke, Chris J Kennedy, Rohan Mehrotra, Stephen Ren, Melody Guan, Orr Zohar, Maren Downing, Hao Wei Chen, Jevin Z Clark, Margaret T Berrigan, Gabriel A Brat, Serena Yeung-Levy

JAMA Surgery (2023)

project

Video-STaR: Bootstrapping Weak Video Supervision for Visual Instruction Tuning

Orr Zohar, Xiaohan Wang, Yonatan Bitton, Idan Szpektor, Serena Yeung-Levy

arXiv preprint (2024)

project arxiv code

VideoAgent: Long-form Video Understanding with Large Language Model as Agent

Xiaohan Wang*, Yuhui Zhang*, Orr Zohar, Serena Yeung-Levy

arXiv preprint (2024)

project arxiv code

Open World Object Detection in the Era of Foundation Models

Orr Zohar, Alejandro Lozano, Shelly Goel, Serena Yeung-Levy, Kuan-Chieh Wang

arXiv preprint (2023)

project arxiv code

LOVM: Language-Only Vision Model Selection

Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, Serena Yeung-Levy

In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (neurIPS 2023)

project proceeding arxiv code

PROB: Probabilistic Objectness for Open World Object Detection

Orr Zohar, Kuan-Chieh Wang, Serena Yeung-Levy

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)

project proceeding arxiv code

Analyzing surgical technique in diverse open surgical videos with multitask machine learning

Emmett D Goodman, Krishna K Patel, Yilun Zhang, William Locke, Chris J Kennedy, Rohan Mehrotra, Stephen Ren, Melody Guan, Orr Zohar, Maren Downing, Hao Wei Chen, Jevin Z Clark, Margaret T Berrigan, Gabriel A Brat, Serena Yeung-Levy

JAMA Surgery (2023)

project

Biointerfaced Sensors for Biodiagnostics

Orr Zohar*, Muhammad Khatib*, Rawan Omar, Rotem Vishinkin, Yoav Y Broza, Hossam Haick

View (2021)

Self-Healing Soft Sensors: From Material Design to Implementation

Muhammad Khatib, Orr Zohar, Hossam Haick

Advanced Materials (2021)

A Multifunctional Electronic Skin Empowered with Damage Mapping and Autonomic Acceleration of Self-Healing in Designated Locations

Muhammad Khatib, Orr Zohar, Walaa Saliba, Hossam Haick

Advanced Materials (2020)

Highly Efficient and Water-Insensitive Self-Healing Elastomer for Wet and Underwater Electronics

Muhammad Khatib, Orr Zohar, Walaa Saliba, Simcha Srebnik, Hossam Haick

Advanced Functional Materials (2020)

Angular Compounding for Speckle Reduction in Optical Coherence Tomography using Geometric Image Registration Algorithm and Digital Focusing

Jingjing Zhao, Yonatan Winetraub, Edwin Yuan, Warren H Chan, Sumaira Z Aasi, Kavita Y Sarin, Orr Zohar, Adam de la Zerda

Scientific Reports (2020)

Epitaxial Superconducting Tunnel Diodes for Light Detection Applications

Krishna Balasubramanian, John Wright, Orr Zohar, Boaz Taitler, Shlomi Bouscher, Huili Grace Xing, Debdeep Jena, Alex Hayat

Optical Materials Express (2020)

Photoresponse above 85 K of Selective Epitaxy Grown High-Tc Superconducting Microwires

Xinxi Xing, Krishna Balasubramanian, Shlomi Bouscher, Orr Zohar, Yuval Nitzav, Amit Kanigel, Alex Hayat

Applied Physics Letters (2020)

Vitæ

Full Resume in PDF.

Website Design, Acknowledgements

You can find all the code needed to build this website in my Github. Feel free to use it, but please link to here, as well as Martin Saveski and Nerfies, whose templates I adapted for the website. Licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.