Amir Bar

Shalom!

I am a Ph.D. candidate at Tel Aviv University and 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]).

Email / Twitter / GitHub / Google Scholar / LinkedIn / CV

News

I'm organizing the first workshop on Prompting in Vision at CVPR 24'. See you in Seattle!
I'm part of the organizing committee of NeurIPS 2023.
I'm currently a part-time student researcher at FAIR, working with Yann LeCun.

Preprints

EgoPet: Egomotion and Interaction Data from an Animal's Perspective
Amir Bar, Arya Bakhtiar, Danny Tran, Antonio Loquercio, Jathushan Rajasegaran, Yann LeCun, Amir Globerson, Trevor Darrell
Technical Report, 2024
Project Page | Data | Code

We present EgoPet, a new egocentric video dataset of animals.

Finding Visual Task Vectors
Alberto Hojel, Yutong Bai, Trevor Darrell, Amir Globerson, Amir Bar
Technical Report, 2024
Code

We find "Task Vectors", latent activations that can guide models to perform computer vision tasks.

IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks
Jiarui Xu, Yossi Gandelsman, Amir Bar, Jianwei Yang, Jianfeng Gao, Trevor Darrell, Xiaolong Wang
Technical Report, 2023
Project Page | Code/Data | Demo

IMProv performs multimodal in-context learning to solve computer vision tasks.

Selected Publications

	Stochastic positional embeddings improve masked image modeling Amir Bar, Florian Bordes, Assaf Shocher, Mahmoud Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann LeCun ICML, 2024 Code Modeling location uncertainties via stochastic positional embeddings (StoP) improve masked image modeling.
	Sequential Modeling Enables Scalable Learning for Large Vision Models Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, Alexei A Efros CVPR, 2024 Project Page \| Code Large Vision Model trained on 420B tokens; exhibits interesting in-context learning capabilities.
	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.
	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
	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.
	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.
`Sample: 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 Code 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.

Medical

	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.
	Improved ICH classification using task-dependent learning Amir Bar, Michal Mauda-Havakuk, Yoni Turner, Michal Safadi, Eldad Elnekave ISBI, 2019 Press Intracranial hemorrhage (ICH) is among the most critical and timesensitive findings to be detected on Head CT. We present a new architecture designed for optimal triaging of Head CTs, with the goal of decreasing the time from CT acquisition to accurate ICH detection. These results are comparable to previously reported results with smaller number of tagged studies.
	Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade Arun Krishnaraj, Spencer Barrett, Orna Bregman-Amitai , Michael Cohen-Sfady, Amir Bar, David Chettrit, Mila Orlovsky, Eldad Elnekave Journal of the American College of Radiology, 2019 Osteoporosis is an underdiagnosed condition despite effective screening modalities. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm.
	PHT-bot: a deep learning based system for automatic risk stratification of COPD patients based upon signs of pulmonary hypertension David Chettrit, Orna Bregman Amitai, Itamar Tamir, Amir Bar and Eldad Elnekave SPIE, 2019 Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. We apply deep learning algorithm to detect those at risk.
	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.

Patents

Systems and methods for automated detection of visual objects in medical images. US Patent 11,776,243
Cross modality training of machine learning models. US Patent 11,587,228.
Identification of a contrast phase depicted in a medical image. US Patent 11,727,087

Template, Last update: 5/2024.

Shalom!

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