Weizmann Institute of Science

20214182

Deep Learning for Computer Vision:
Fundamentals and Applications
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Final Project Overview

The goal of this is project is for you to implement a Deep Learning project of your own. You are expected to leverage the knowledge and techniques taught in this course to perform a task in the Computer Vision domain. You may either choose aproject from the default project list or pursue an idea of your own. You are particularly welcome to choose tasks that will benefit your lab’s research or follow your personal interests. You will conclude your work by submitting a Project Report.

Timeline:

Additional information

Default Projects

Spot the fake

training set
validation set
test set I, targets, map.
test set II (additional category), targets, map.

InstaRepeat

training set
test set

Spring 2021 Students Projects

Project Title Student Names Lecture Link
Session 1: DL for Medical Imaging
Pleural Line Localization in Lung Ultrasound Oz Frank and Alon Mamistvalov YouTube Link
Brain Tumors Segmentation Itai Antebi, Ronen Reshef and Dan Segev YouTube Link
Predicting Microbial Alpha Diversity Based on Retinal Imaging Liron Zahavi, Yochai Edlitz and Noam Bar YouTube Link
Session 2: Other Modalities
Anomaly Detection in PCB X-ray Images Gil Boazi and Matan Schlanger YouTube Link
Predicting Behavioral Signals from fMRI Tamir Scherf and David Ungarish YouTube Link
Nucleus Prediction from Brightfield Video Joseph Steinberger and Gal Dekel YouTube Link
Session 3: Super Resolution
Improving Image Super Resolution Ophir Sarusi and Benjamin Brazowski YouTube Link
A Discrete and Beyond-Convolutional Approach to ZSSR Dror Bar, Gilad Ben Uziyahu and Asaf Petruschka YouTube Link
ZSSR-U: "Zero-Shot" Super-Resolution in Ultrasound Imaging Or Bar-Shira and Yair Ben-Sahel YouTube Link
Session 4:
Spot the Fake David Sriker, Yonatan Bachar and Michael Lelouch YouTube Link
InstaRepeat Ron Mosenzon, Ofir Raz and Yaniv Shahar YouTube Link
Meta learning for Scene-Text-Recognition on New Languages Bar Karov, Shiri Moshe and Yonatan Sverdlov YouTube Link
Session 5: Beyond 2D
Time-Consistent Underwater Color Enhancement for Videos Hanan Mordechai, Dana Joffe and Shira Werman YouTube Link
Video Compression Using Implicit Neural Representations Chaya Barbolin and Itsik Shapira YouTube Link
Feature Visualization and Understanding of 3D Point Clouds Deep Neural Networks Hodaya Koslowsky, Michal Skoury and Yuval Belfer YouTube Link
Rethinking Sanity Checks for Saliency Methods Gal Yona YouTube Link
Session 6: ViT
Image Style Transfer using a ViT Descriptor Dolev Ofri, Rafail Fridman and Narek Tumanyan YouTube Link
Dust Levels Predictions Using ViT Dori Nissenbaum YouTube Link
Vision Transformers for Microscopy (MIBI) Data Omer Bar Tal and Hido Pinto YouTube Link

 

 

The template of this website is based on CSAIL MIT's Advanced Computer Vision course