Amir Bar

I am a first-year Ph.D. student at Tel Aviv University and a visiting student at Berkeley AI Research working with Amir Globerson and Trevor Darrell. My goal is to develop structured representations that can better capture semantics in images and video.

Before, I was an AI Research Lead at Zebra Medical Vision, where I led the development of algorithms in CT scans. Few of my team's algorithms were approved for clinical use by the FDA and they are adopted at hospitals around the world. Prior to that I obtained my Master's from Tel Aviv University and worked with Lior Wolf.

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Selected Publications

DETReg: Unsupervised Pretraining with Region Priors for Object Detection    
Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson
In submission
Project Page | Code

Pretraining transformers to localize potential objects improves object detection.

Compositional Video Synthesis with Action Graphs    
Amir Bar*, Roei Herzig*, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson
ICML, 2021
Project Page | Code | Video

We introduce Action Graphs, a structure that can better capture the compositional and hierrchical nature of actions. We propose a goal-oriented video synthesis task of *Action Graph to Video*

* Equally contributed.

Learning Canonical Representations for Scene Graph to Image Generation
Roei Herzig*, Amir Bar*, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson
ECCV, 2020
Project Page | Code | Video

We present a model for Scene Graph to Image generation which is more robust to complex input scene graphs.

* Equally contributed.

3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT
David Chettrit, Tomer Meir, Hila Lebel, Mila Orlovsky, Ronen Gordon, Ayelet Akselrod-Ballin*, Amir Bar*
MICCAI, 2020
Press 1 2

We present a novel architecture used to detect vertebral compression fractures in Chest and Abdomen CT.

* Equally advised.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization    
Noa Dagan, Eldad Elnekave, Noam Barda, Orna Bregman-Amitai, Amir Bar, Mila Orlovsky, Eitan Bachmat & Ran D. Balicer
Nature Medicine, 2020
Press 1 2

Methods for identifying patients at high risk for osteoporotic fractures are underutilized. We demonstrate it is feasibile to automatically evaluate risk based on routine abdomen or chest computed tomography (CT) scan.

Learning Individual Styles of Conversational Gesture  
Shiry Ginosar*, Amir Bar*, Gefen Kohavi, Caroline Chan, Andrew Owens, Jitendra Malik
CVPR, 2019
Press | Project Page | Code | Data

We predict plausible gestures to go along with someone's speech.

* Equally contributed.


The man allowed that about health captain played that alleged to Marks live up in the club comes the handed up moved to a brief

Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Ofir Press*, Amir Bar*, Ben Bogin*, Jonathan Berant, Lior Wolf
1st Workshop on Learning to Generate Natural Language at ICML, 2017

We show that recurrent neural networks can be trained to generate text with GANs from scratch and vastly improve the quality of generated sequences compared to a convolutional baseline.

* Equally contributed.

Compression Fractures Detection on CT
Amir Bar, Lior Wolf, Orna Bregman Amitai, Eyal Toledano, Eldad Elnekave
SPIE, 2017
Press 1 2

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans.


Meet my dog taco on instagram :)

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