General Program

Tal Perl
Computer Vision Algorithm Team Leader
Eyesight Technologies

Bio

Tal Perl is a computer vision algorithm team leader at EyeSight. His main focus is in geometrical deep learning, specifically estimating 3D properties using 2D images. He holds a BSc and MSc in electrical engineering from Tel Aviv University.

Abstract

Face alignment is a known computer vision challenge that has been explored enthusiastically in the past two decades. Thanks to the deep learning paradigm, researchers are now able to train networks to estimate 3D poses of objects using large amounts of data. A recurring limitation in these solutions is the lack of labelled data. Moreover, it is challenging to annotate 2D images with 3D labels. In this talk, we review the latest works on this topic and present how we generated massive amounts of semi-synthetic data using 3D morphable models. With this data, we were able to train a competitive real-time face tracker that is also lightweight and highly accurate for the task of head pose estimation.

Dr. Tammy Riklin Raviv
Faculty Member, School of Electrical and Computer Engineering
Ben-Gurion University

Bio

Tammy Riklin Raviv is a faculty member in the School of Electrical and Computer Engineering of Ben-Gurion University. Her main research interests are Computer Vision, Machine Learning and Biomedical Image Analysis. She is an associate editor at the IEEE Transactions on Medical Imaging (TMI) Journal and a TC member at the IEEE Bio Imaging and Signal Processing (BISP) Committee. She holds a B.Sc. in Physics and an M.Sc. in Computer Science (both magna cum laude) from the Hebrew University of Jerusalem. She received her Ph.D. from the School of Electrical Engineering of Tel-Aviv University. During 2010-2012 she was a research fellow at Harvard Medical School and the Broad Institute of MIT and Harvard. Prior to this (2008-2010) she was a post-doctorate associate in CSAIL, MIT.

Abstract

In this talk I will introduce a novel Deep Learning framework, which quantitatively estimates image segmentation quality without the need for human inspection or labeling. We refer to this method as a Quality Assurance Network - QANet. Specifically, given an image and a ‘proposed’ corresponding segmentation, obtained by any method including manual annotation, the QANet solves a regression problem in order to estimate a predefined quality measure (or example the IoU or a Dice score) with respect to the unknown ground truth. The QANet is by no means yet another segmentation method. Instead, it performs a multi-level, multi-feature comparison of an image-segmentation pair based on a unique network architecture, called the RibCage.

To demonstrate the strength of the QANet, we addressed the evaluation of instance segmentation using two different datasets from different domains, namely, high throughput live cell microscopy images from the Cell Segmentation Benchmark and natural images of plants from the Leaf Segmentation Challenge. While synthesized segmentations were used to train the QANet, it was tested on segmentations obtained by publicly available methods that participated in the different challenges. We show that the QANet accurately estimates the scores of the evaluated segmentations with respect to the hidden ground truth, as published by the challenges’ organizers.

• Yakov Miron
• Research Scientist
• Elbit Systems Aerospace
•  

Bio

Computer Vision & Deep Learning Researcher Elbit Systems Aerospace. Yakov Miron is a BScEE from Ben-Gurion university and an MScEE from Tel Aviv university in Israel. He was working for Motorola Inc. and Silentium as an algorithm developer. His current position is Computer Vision and Deep Learning Researcher in the R&D division at Elbit Systems. His interest topics are Machine Learning, Deep Learning, Computer Vision, 3D Modeling, as well as Navigation, Localization and SLAM.

Abstract

This work offers a new method for generating photo-realistic images from semantic label maps and a simulator edge map images. We do so in a conditional manner, where we train a Generative Adversarial network (GAN) given an image and its semantic label map to output a photo-realistic version of that scene. Existing architectures of GANs still lack the photo-realism capabilities. We address this issue by embedding edge maps, and presenting the Generator with an edge map image as a prior, which enables generating high level details in the image. We offer a model that uses this generator to create visually appealing videos as well, when a sequence of images is given.

Hagai Lalazar
Stealth-mode Startup

Bio

Hagai Lalazar is a computational neuroscientist and entrepreneur, with over 20 years experience in neuroscience research and innovative startups in Tel-Aviv and Silicon Valley. He completed his BA in Math and Cognitive Science at UC Berkeley, PhD in neural computation at the Hebrew University, and Postdoc at the Center for Theoretical Neuroscience at Columbia. He’s an expert in motor neuroscience and Brain-Machine Interfaces. He was CTO and Co-Founder of System1 Biosciences, a startup developing a robotic platform for growing 3D brain cultures for drug discovery. He is currently CEO and Co-Founder of a stealth-mode startup, developing breakthrough neuro-technology powered by deep learning.

Abstract

We are at the forefront of a revolution in how we interact with the world. Brain-Machine Interfaces (BMI) is technology that uses neural signals and advanced algorithms to decode the brain, enabling interactions with devices using only your mind. Recent advances in BMI are based on progress in invasive and non-invasive sensors, and the marriage of neuroscience and machine learning. In the next few years, invasive BMI applications will restore movement to paralyzed patients, enable stroke patients to speak, and enable the blind to see. Non-invasive applications will change the way we interact with devices, digital experiences, entertainment, and mental healthcare.

Ori Shachar
Chief Science Officer & Head of AI
SeeTree

Bio

I am leading the science, computer vision and ML activities of SEETEE.AI, a new successful start-up in the agtech domain, since its pre-seed development. As former senior director in algorithms and R&D in Mobileye, I leverage 13 years of experience in Mobileye to help introduce a similar transformation to the agtech world, together with an excellent multidisciplinary team.    

Abstract

We introduce a new method of image-registration, named "semantic spatial alignment" (SSA).

This method performs an optimization of the semantic difference loss between two images, using a gradient-descend-process which optimizes the parameters of a neural-network composed of a single differentiable spatial-transformer. This new method shows a dramatic improvement over state-of-the-art feature-point-matching methods (e.g SIFT, ORB), when inputs are time-repeating orthomosaics of tree-plantations, where inputs can be from different sensors and resolutions, and contain changes in the shape of the tree objects. The method is also superior in cases where the success of affine, projective or other simple homographic transformation maps are limited. The method shows a successful use of deep learning in dramatically improving a traditional "classical computer vision task" as image-registration.

Dr. Leonid Karlinsky
Senior Research Scientist, Research Team Lead
IBM Research AI

Bio

Leonid Karlinsky leads the CV & DL research team in the Computer Vision and Augmented Reality (CVAR) group @ IBM Research AI. Before joining IBM, he served as a research scientist in Applied Materials, Elbit, and FDNA. He is actively publishing and reviewing at ECCV, ICCV, CVPR and NeurIPS, and is serving as an IMVC steering committee member for the past 3 years. His recent research is in the areas of few-shot learning with specific focus on object detection, metric learning, and example synthesis methods. He received his PhD degree at the Weizmann Institute of Science, supervised by Prof. Shimon Ullman.

Abstract

In this talk we will discuss our recent advances in few-shot learning, a regime where only a handful of training examples (maybe just one) are available for learning novel categories unseen during training. We will cover a method for few-shot classification that is capable of matching and localizing instances of novel categories, despite being trained and used with only category level image labels and without any location supervision, also opening the door for weakly supervised few-shot detection. We will cover a method for meta-learning a model that automatically modifies its architecture to better adapt to novel few-shot tasks. Finally, we will discuss the limitation of the current few-shot learning methods when handling extreme cases of domain transfer, and offer a new benchmark and some ideas towards cross-domain few-shot learning.

Michal Holtzman Gazit
Lead Computer Vision Researcher
Novocure

Bio

Michal Holtzman Gazit is a lead computer vision researcher in Novocure, with nearly 20 years of experience in the field of computer vision and image processing and medical images.  She received her BSc. (1998) and MSc. (2004) in Electrical Engineering Technion, and her PhD (2010) in Computer Science, Technion. During 2010-2012, she was a post-doctorate fellow in the computer science department in the University of British Columbia, Vancouver, Canada. Her main research interests are computer vision, image processing, AI in healthcare and deep learning.

Abstract

Tumor Treating Fields (TTFields) is an FDA approved treatment for specific types of cancer and significantly extends patients’ life. The intensity of the TTFields within the tumor is associated with the treatment outcomes: the larger the intensity, the longer the patients are likely to survive.  This requires optimizing TTFields transducer array location such that their intensity is maximized around the tumor. Finding the best array placement is a multi-stage computationally expensive optimization problem. Here, we present a novel method that incorporates machine learning and deep learning in order to allow physicians a better TTFields treatment planning.

Dan Levy
Staff Researcher at the Smart Sensing and Vision Group
General Motors R&D Israel

Bio

I am a Staff Researcher at the Smart Sensing and Vision group, General Motors R&D Israel, in the fields of computer vision and machine learning. I received his B.Sc. degree (with honor) in mathematics and computer science from the Tel-Aviv University, in 2000, and the M.Sc. and PhD degrees in applied mathematics and computer science at the Weizmann Institute, in 2004 and 2009 respectively. In the Weizmann Institute I conducted research in human and computer vision under the supervision of Professor Shimon Ullman. Since 2007 I have been conducting industrial computer vision research and development at several companies including General Motors and Elbit Systems, Israel.

Abstract

We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with on-board sensing without assuming a known constant lane width or relying on pre-mapped environments. Our network architecture, 3D-LaneNet, applies two new concepts: intra-network inverse-perspective mapping (IPM) and anchor-based lane representation. The intra-network IPM projection facilitates a dual-representation information flow in both regular image-view and top-view. An anchor-per-column output representation enables our end-to-end approach which replaces common heuristics such as clustering and outlier rejection, casting lane estimation as an object detection problem. In addition, our approach explicitly handles complex situations such as lane merges and splits. Results are shown on two new 3D lane datasets, a synthetic and a real one. For comparison with existing methods, we test our approach on the image-only tuSimple lane detection benchmark, achieving performance competitive with state-of-the-art.

Uriya Levy
Computer Vision Researcher
Rafael

Bio

Uriya is currently a computer vision researcher at Rafael. He has worked on noise removal from imagery for noise-sensitive sensors and on change detection. His current research is focused on unsupervised change detection on aerial images, based on metric-learning.

He has an Electrical Engineering M.Sc. from Tel Aviv university, specializing in computer vision algorithms and software development.

Abstract

Given a pair of images of the same geographic area taken at different times, we wish to detect changes between them. Change detection is a challenging task. It is required to distinguish between fundamental changes, often man made, and insignificant natural ones. The latter may result from changing lighting, weather, camera pose, slight vegetation movement due to wind, and small errors in image registration. We address the change detection problem by training a learned descriptor using registered image pairs. Our fully convolutional CNN-based descriptor can efficiently detect changes in large aerial image pairs. It is shown to generalize well for a completely new scene and type of changes, while being robust to registration errors. The labeling of each image pair as similar or different is implied by the automatic registration process. Therefore, no manual annotation of any kind is required. While the lack of supervision results in label noise, the algorithm proves highly robust to it.

Dr. Anna Levant
3D Metrology Algorithm Team Leader
Applied Materials

Bio

Dr. Anna Levant is a 3D metrology algorithm team leader at Applied Materials. She holds her PhD degree from Weizmann Institute of Science in Applied Mathematics, specifically Chaos problem.  Prior to joining Applied Materials, she worked for 10 years in various medical devices companies leading the development of algorithms for various modalities as MRI, X-ray, ECG etc.

Abstract

3D metrology is a new fascinating field in the semiconductor industry. Shrinkage of planar devices has reached its physical limit and advanced nodes resort to 3D design to increase the feature density in the device. Reliable measurements of these 3D structures are crucial for a chip development process.

We propose a novel supervised ML (Machine Learning) based solution for inferring 3D structure from 2D SEM (Scanning Electron Microscope) images. Our algorithm reached sub-nanometer accuracy and high precision.

The generality of our method and its ability to extract hidden information from SEM images open the door to a plethora of applications in 3D metrology for memory and logic devices.

Sahar Froim
Algorithm Developer
WSC Sports

Bio

Sahar Froim is a senior algorithm developer at WSC Sports, focusing on the fascinating world of machine-learning in sports broadcasting. Sahar is currently pursuing a Ph.D. at the School of Electrical Engineering in Tel-Aviv University, where he studies the intersection between machine-learning & optics. Prior to WSC Sports, Sahar worked at Qualcomm for 4 years, while completing his B.Sc. & M.Sc. in Electrical Engineering (magna cum laude) at Tel-Aviv University. Sahar served for 6 years as an officer in an elite Israeli intelligence unit, leading a team of tech researchers.

Abstract

WSC Sports’ main goal is developing sports’ action detection and event recognition algorithms with a very high success rate, in order to automatically generate highlight videos from sports broadcasts. Due to its high recall rates, an object detector is a commonly used tool in our proprietary action detection algorithms

Its main limitation is its precision rate on a sports broadcast, where it gets “real world” images, which produce false positives.

In this presentation, we propose a method for improving object detection models’ precision, without enlarging the train set, by using preceding and successive convolutional neural networks.

Ovadya Menadeva
Computer Vision Department Manager
Percepto 

Bio

Ovadya joined Percepto on January 2019 as a Computer Vision team leader. With over 20 years of experience building Computer Vision solutions in the industry

with companies such as Intel Corporation, Applied Materials and PointGrab. Ovadya’s last position was with Innoviz-Tech, headed the Computer Vision department. Ovadya set the foundation for the Innoviz-Tech Computer Vision department, including defining the computer vision product specs Ovadya has vast experience in Computer Vision applications, including Deep learning, Object detection and tracking in mass production such as Samsung TV.

Ovadya holds an Msc. degree in the field of computer vision from the Weizmann Institute of Science.

Abstract

Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection. We demonstrate this concept on solar panel installation. A deep learning, data-driven algorithm for identifying changes based on a change detection deep learning  algorithm was proposed. We use the Conditional Adversarial Network approach to present a framework for change detection in images. The proposed network architecture is based on pix2pix GAN framework. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art change detection methods.

Dr. Amir Handelman
Senior Lecturer at Faculty of Electrical Engineering
Holon Institute of Technology

Bio

Dr. Amir Handelman received his BSc, MSc and PhD degrees in Electrical Engineering in 2008, 2011 and 2014, respectively, all from Tel-Aviv University, Israel. In 2014, Amir joined the faculty of Electrical Engineering in Holon Institute of Technology (HIT) as a tenure-track faculty member and established there the Applied Optics and Machine Vision Lab. In addition to his academic background, Amir has over 10 years' experience in computer vision and optics, which he gained during his works in several Hi-Tech companies, such as Israel Aerospace Industries (IAI), Volume-Elements Ltd., and KLA-Tencor.

Abstract

Today, there is an increasing desire by patients and hospital managers to make trainings for residents and surgeons before performing actual operations. This desire made modern surgery syllabus to include use of simulators for improving surgical skills in teaching programs. In this talk I will review our computerized algorithms aim to score the performance of laparoscopic cutting and suturing operation in both general surgery and ophthamology medical fields . Using our algorithms, human assessment is not necessary and the quality of the surgery outcomes is objectively evaluated. 

Leah Bar
MaxQ-AI and Tel Aviv University

Bio

Leah Bar holds B.Sc. in Physics, M.Sc. in Bio-Medical Engineering and PhD in Electrical Engineering from Tel-Aviv University.
She worked as a post-doctoral fellow in the Department of Electrical Engineering at the University of Minnesota.
She is currently a senior researcher at MaxQ-AI, a medical AI start-up, and in addition a researcher at the Mathematics Department in Tel-Aviv University.
Her research interest are: machine learning, image processing, computer vision and variational methods.

Abstract

We introduce a novel neural network-based partial differential equations solver for forward and inverse problems. The solver is grid free, mesh free and shape free, and the solution is approximated by a neural network.
We employ an unsupervised approach such that the input to the network is a points set in an arbitrary domain, and the output is the set of the corresponding function values. The network is trained to minimize deviations of the learned function from the PDE solution and satisfy the boundary conditions.
The resulting solution in turn is an explicit smooth differentiable function with a known analytical form.
Unlike other numerical methods such as finite differences and finite elements, the derivatives of the desired function can be analytically calculated to any order. This framework therefore, enables the solution of high order non-linear PDEs. The proposed algorithm is a unified formulation of both forward and inverse problems where the optimized loss function consists of few elements: fidelity terms of L2 and L infinity norms, boundary and initial conditions constraints, and additional regularizers. This setting is flexible in the sense that regularizers can be tailored to specific problems. We demonstrate our method on several free shape 2D second order systems with application to Electrical Impedance Tomography (EIT). 

Lior Fritz
Applied Scientist
Amazon

Bio

Lior is an Applied Scientist at Amazon. Since 2015, he has been working with Deep Learning algorithms for Computer Vision and Natural Language Processing tasks. Lior was in the development team of the "Alexa, how do I look?" feature, delivered to Echo Look and Amazon Shopping App customers.
Lior holds a BSc and MSc in Electrical Engineering from Tel-Aviv University. His thesis research focused on Speech Recognition with Neural Networks.

Abstract

Understanding 3D human pose from images is a challenging task, but with a large number of potential applications. In this talk, we will present our work on looking beyond the weak perspective assumptions, usually employed in the conventional approaches to 3D human pose estimation. Through experimentation on the 3D poses in the wild (3DPW) dataset, we show that using full perspective projection, with the correct camera center and an approximated focal length, provides favorable results over existing methods. Our algorithm has resulted in a winning entry for the 3DPW Challenge, reaching first place in joints orientation accuracy.

• Dr. Elad Levi
  Senior Machine Learning Researcher
  Nexar
•  

Bio

Elad Levi is a machine learning researcher at Nexar, a startup aiming to create a real-time traffic network for shaping the future of mobility. His work focuses on leveraging Nexar's large-scale datasets of real-world driving environments to mapping and automotive safety applications. Elad received a PhD degree in mathematics from the Hebrew University. His thesis was in the field of model-theoretic with applications to combinatorics problems. 

Abstract

Building fresh accurate maps of road items is a key ingredient in smart cities management and enabling fully autonomous vehicles. Building such maps from chip sensors such as monocular camera, GPS sensor and IMU, is a major challenge. It is even harder doing it in crowdsourcing setting, where the data is noisy and the camera position is arbitrary and unknown.

In this talk, we address this problem and related issues, namely; Camera alignment, self-localization, depth estimation, etc’. We demonstrate that using self-supervised approaches along with large corpus of diverse noisy-unlabeled data, we can get surprisingly accurate results.

Adi Szeskin
AI & Data Science Researcher
Intel

Bio

Adi is a member of the core AI & data science research team of Intel’s Advanced Analytics group (Deep learning, NLP and computer vision research for sales and marketing, manufacturing, healthcare), in parallel to PhD research at the Hebrew University’s Computer Science department, supervised by Prof. Leo Joskowicz.

Adi holds an M.Sc in Bio-Engineering, an M.E. in Bio-Medical Engineering and a B.Sc in Electronics engineering.

Abstract

We present a deep learning system for testing graphics units by detecting novel visual corruptions in videos. Unlike previous work in which manual tagging was required to collect labeled training data, our weak supervision method is fully automatic and needs no human labelling. This is achieved by reproducing driver bugs that increase the probability of generating corruptions, and by making use of ideas and methods from the Multiple Instance Learning (MIL) setting. In our experiments, we significantly outperform self-supervised methods such as GAN-based models and discover novel corruptions undetected by baselines, while adhering to strict requirements on accuracy and efficiency of our real-time system.

Jeff Mather
Engineering Manager – Image Processing
MathWorks

Bio

Jeff Mather is a senior software engineer and the development manager of the Image Processing Toolbox. He has managed the team since 2013 and has developed features for the toolbox and MATLAB since 2000, particularly in the area of file formats, medical image processing, HDR imaging, color science, and software performance optimization. He has an undergraduate degree in mathematics from Grinnell College and a Master of Software Engineering from Brandeis University. He has been with MathWorks since 1998.

Abstract

We will explore the challenges of processing very large pathology images and present a framework to solve problems in segmentation, feature extraction, measurement, and labeling. The solution exploits multiple resolution levels, parallelism, and conditional processing. Special attention is given to using deep convolutional neural networks.

Mattan Serry
Data Scientist
Microsoft

Bio

Mattan Serry is a data scientist at Microsoft Media AI Research, where he is working on Video Indexer, an Azure service for video analytics.

He holds a bachelor’s degree in computer engineering and a master’s degree in computer science, both from Tel Aviv University.

His thesis work is in the fields of computer vision, deep learning, and 3D understanding, under the supervision of Dr. Amit Bermano.

Before joining Microsoft in 2019, he also worked at Apple and Sony Semiconductor Israel.

Abstract

Face and head detection are fundamental problems in computer vision and related areas.
They are a common thread in contemporary applications such as augmented reality, biometric authentication, and social media.

The work presented in this lecture is a novel approach for head localization.

The suggested algorithm infers a person's head location given a prior face detection and other extracted features from the image.

Some features are mandatory and some are optional.

The algorithm uses classical methods – and thus it excels in computational efficiency – to solve the 2D to 2D problem via a 3D person polygon mesh, by understanding the most appropriate model and transformation.

Eyal Ziv
Senior Machine Learning & Computer Vision Researcher
Zebra Medical Vision

Bio

Eyal Ziv is a senior ML and computer vision researcher at the R&D team of Zebra Medical Vision.

Eyal’s research is focusing on classification and detection of lesions in x-ray images.

Prior to joining Zebra Medical Vision, he worked as a ML researcher in the Aerospace division of Elbit Systems, developing algorithms for autonomous platforms. Eyal Ziv holds his BSc in Aerospace Engineering from the Technion institute.

His topics of interest are multimodal learning and meta learning.

Abstract

The chest radiography is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. However, there is an immense world-wide decrease in the number of physicians capable of providing their rapid and accurate interpretation.  With 2 billion people joining the middle class worldwide and a growing global shortage of clinical experts, there is a sense of urgency to develop technologies which can help bridge the gap between supply and demand of radiology services.

Here we will review the research and development of Zebra-Medical AI based solutions aimed at providing automated and scalable diagnostic support in interpretation of chest radiographs.

We'll demonstrate the application of our technology on real life clinical examples where such solutions have impacted patients' care by substantially reducing time to treatment and preventing misdiagnosis.