activity
20182022
most citedRethinking FUN: Frequency-Domain Utilization Networks

10 citations · 14 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

15 papers · 1 filter

cs.CV2022

Aggregating Layers for Deepfake Detection

Amir Jevnisek, Shai Avidan

The increasing popularity of facial manipulation (Deepfakes) and synthetic face creation raises the need to develop robust forgery detection solutions. Crucially, most work in this…

cs.CV20221 cited

How Low Can We Go? Pixel Annotation for Semantic Segmentation

Daniel Kigli, Ariel Shamir, Shai Avidan

How many labeled pixels are needed to segment an image, without any prior knowledge? We conduct an experiment to answer this question. In our experiment, an Oracle is using Active…

cs.CV2021

DeepBBS: Deep Best Buddies for Point Cloud Registration

Itan Hezroni, Amnon Drory, Raja Giryes +1

Recently, several deep learning approaches have been proposed for point cloud registration. These methods train a network to generate a representation that helps finding matching p…

cs.CV20212 cited

Reducing ReLU Count for Privacy-Preserving CNN Speedup

Inbar Helbitz, Shai Avidan

Privacy-Preserving Machine Learning algorithms must balance classification accuracy with data privacy. This can be done using a combination of cryptographic and machine learning to…

cs.CV202010 cited

Rethinking FUN: Frequency-Domain Utilization Networks

Kfir Goldberg, Stav Shapiro, Elad Richardson +1

The search for efficient neural network architectures has gained much focus in recent years, where modern architectures focus not only on accuracy but also on inference time and mo…

cs.CV2020

Geometric Adversarial Attacks and Defenses on 3D Point Clouds

Itai Lang, Uriel Kotlicki, Shai Avidan

Deep neural networks are prone to adversarial examples that maliciously alter the network's outcome. Due to the increasing popularity of 3D sensors in safety-critical systems and t…