Amir Bar


I am a Ph.D. candidate at Tel Aviv University and a Visiting Student Researcher in BAIR working with Amir Globerson and Trevor Darrell. My goal is to teach computers to understand the world from unlabeled data, using little to no supervision.

I've pioneered AI for medical imaging as an AI Research Lead at Zebra Medical Vision (acquired). My research team developed multiple FDA cleared algorithms for automatic analysis of medical images (e.g, [1, 2]) . Before that, I obtained my Master's from Tel Aviv University and worked with Lior Wolf.

I'm looking for undergraduate students who are interested to join exciting research projects and publish in top-tier CV & ML conferences.

Email  /  Twitter  /  GitHub  /  Google Scholar  /  LinkedIn

  • I'm currently a part-time student researcher at FAIR, working with Yann LeCun.
Selected Publications

Visual Prompting via Image Inpainting    
Amir Bar*, Yossi Gandelsman*, Trevor Darrell, Amir Globerson, Alexei A. Efros
NeurIPS, 2022
Project Page | Code/Data

Adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification.

* Equally contributed.
fast-texture Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson
NeurIPS, 2022
Project Page | Code
Incorporating image level scene structure during training improves video transformers.

Winner of the Ego4D CVPR'22 PNR Temporal Localization Challenge
fast-texture Object-Region Video Transformers
Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik,
Anna Rohrbach, Trevor Darrell, Amir Globerson
CVPR, 2022
Project Page | Code

Incorporating objects into transformer layers improves video transformers.

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
CVPR, 2022
Project Page | Code | Video | Demo

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.


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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.


Check out Hadar's website and meet Taco on instagram :)

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