Search Results for author: Shai Avidan

Found 33 papers, 14 papers with code

Stress-Testing LiDAR Registration

1 code implementation16 Apr 2022 Amnon Drory, Shai Avidan, Raja Giryes

The standard benchmark for this setting, KITTI-10m, has essentially been saturated by recent algorithms: many of them achieve near-perfect recall.

Autonomous Driving Frame +1

How Low Can We Go? Pixel Annotation for Semantic Segmentation

no code implementations25 Jan 2022 Daniel Kigli, Ariel Shamir, Shai Avidan

Based on this single-image-annotation experiment, we design an experiment to quickly annotate an entire data set.

Active Learning Semantic Segmentation

Transformaly -- Two (Feature Spaces) Are Better Than One

1 code implementation8 Dec 2021 Matan Jacob Cohen, Shai Avidan

Anomaly detection is a well-established research area that seeks to identify samples outside of a predetermined distribution.

 Ranked #1 on Anomaly Detection on One-class CIFAR-100 (using extra training data)

Anomaly Detection

Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models

no code implementations21 Nov 2021 Alon Zolfi, Shai Avidan, Yuval Elovici, Asaf Shabtai

In addition, we validated our adversarial mask effectiveness in real-world experiments by printing the adversarial pattern on a fabric medical face mask, causing the FR system to identify only 3. 34% of the participants wearing the mask (compared to a minimum of 83. 34% with other evaluated masks).

Face Recognition Real-World Adversarial Attack

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction

1 code implementation16 Oct 2021 Itai Lang, Dvir Ginzburg, Shai Avidan, Dan Raviv

We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction.

3D Dense Shape Correspondence

DeepBBS: Deep Best Buddies for Point Cloud Registration

1 code implementation6 Oct 2021 Itan Hezroni, Amnon Drory, Raja Giryes, Shai Avidan

The Best Buddies criterion is a strong indication for correct matches that, in turn, leads to accurate registration.

Point Cloud Registration

kNet: A Deep kNN Network To Handle Label Noise

no code implementations20 Jul 2021 Itzik Mizrahi, Shai Avidan

To use kNet, we first train a preliminary network on the data set, and then train kNet on the penultimate layer of the preliminary network. We find that kNet gives a smooth approximation of kNN, and cannot handle the sharp label changes between samples that kNN can exhibit.

Reducing ReLU Count for Privacy-Preserving CNN Speedup

no code implementations28 Jan 2021 Inbar Helbitz, Shai Avidan

Experiments on several datasets reveal that we can cut the number of ReLU operations by up to three orders of magnitude and, as a result, cut the communication bandwidth by more than 50%.

Geometric Adversarial Attacks and Defenses on 3D Point Clouds

1 code implementation10 Dec 2020 Itai Lang, Uriel Kotlicki, Shai Avidan

Additionally, we demonstrate the robustness of our attack in the case of defense, where we show that remnant characteristics of the target shape are still present at the output after applying the defense to the adversarial input.

Adversarial Attack Adversarial Defense

Rethinking FUN: Frequency-Domain Utilization Networks

1 code implementation6 Dec 2020 Kfir Goldberg, Stav Shapiro, Elad Richardson, Shai Avidan

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 model size.

Best Buddies Registration for Point Clouds

1 code implementation5 Oct 2020 Amnon Drory, Tal Shomer, Shai Avidan, Raja Giryes

We propose new, and robust, loss functions for the point cloud registration problem.

Point Cloud Registration Template Matching

Co-occurrence Based Texture Synthesis

1 code implementation17 May 2020 Anna Darzi, Itai Lang, Ashutosh Taklikar, Hadar Averbuch-Elor, Shai Avidan

As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.

Image Generation Texture Classification +1

Proximity Preserving Binary Code using Signed Graph-Cut

no code implementations5 Feb 2020 Inbal Lav, Shai Avidan, Yoram Singer, Yacov Hel-Or

We show that the proposed approximation is superior to the commonly used spectral methods with respect to both accuracy and complexity.

graph partitioning

Deep Image Compression using Decoder Side Information

3 code implementations ECCV 2020 Sharon Ayzik, Shai Avidan

We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase.

Image Compression

CrowdCam: Dynamic Region Segmentation

no code implementations28 Nov 2018 Nir Zarrabi, Shai Avidan, Yael Moses

We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane.

Dynamic Region Segmentation

Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

1 code implementation4 Nov 2018 Dana Berman, Deborah Levy, Shai Avidan, Tali treibitz

The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult.

Image Dehazing Image Enhancement +1

The Resistance to Label Noise in K-NN and DNN Depends on its Concentration

no code implementations30 Mar 2018 Amnon Drory, Oria Ratzon, Shai Avidan, Raja Giryes

We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise.

General Classification Multi-class Classification

Co-occurrence Filter

no code implementations CVPR 2017 Roy J Jevnisek, Shai Avidan

It is based on the Bilateral Filter (BF) but instead of using a Gaussian on the range values to preserve edges it relies on a co-occurrence matrix.

Detecting Moving Regions in CrowdCam Images

no code implementations10 Nov 2016 Adi Dafni, Yael Moses, Shai Avidan

We address the novel problem of detecting dynamic regions in CrowdCam images, a set of still images captured by a group of people.

Best-Buddies Tracking

no code implementations1 Nov 2016 Shaul Oron, Denis Suhanov, Shai Avidan

BBS was introduced as a similarity measure between two point sets and was shown to be very effective for template matching.

Template Matching

Non-Local Image Dehazing

no code implementations CVPR 2016 Dana Berman, Tali treibitz, Shai Avidan

This dependency is expressed in the transmission coefficients, that control the scene attenuation and amount of haze in every pixel.

Image Dehazing Single Image Dehazing

Peeking Template Matching for Depth Extension

no code implementations ICCV 2015 Simon Korman, Eyal Ofek, Shai Avidan

We demonstrate on real-world data that our algorithm is capable of completing a full 3D scene from a single depth image and can synthesize a full depth map from a novel viewpoint of the scene.

Template Matching

Spatially Coherent Random Forests

no code implementations9 Nov 2015 Tal Remez, Shai Avidan

Each tree in the forest produces a segmentation of the image plane and the boundaries of the segmentations of all trees are aggregated to produce a final hierarchical contour map.

Stereo on a budget

no code implementations28 Apr 2014 Dana Menaker, Shai Avidan

Instead of having both cameras send their entire image to the host computer, the left camera sends its image to the host while the right camera sends only a fraction $\epsilon$ of its image.

Image Compression Stereo Matching +1

FasT-Match: Fast Affine Template Matching

no code implementations CVPR 2013 Simon Korman, Daniel Reichman, Gilad Tsur, Shai Avidan

Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure.

Template Matching

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