Search Results for author: Shai Avidan

Found 43 papers, 24 papers with code

Fixed-point Inversion for Text-to-image diffusion models

no code implementations19 Dec 2023 Barak Meiri, Dvir Samuel, Nir Darshan, Gal Chechik, Shai Avidan, Rami Ben-Ari

Several applications of these models, including image editing interpolation, and semantic augmentation, require diffusion inversion.

Optimize and Reduce: A Top-Down Approach for Image Vectorization

1 code implementation18 Dec 2023 Or Hirschorn, Amir Jevnisek, Shai Avidan

Vector image representation is a popular choice when editability and flexibility in resolution are desired.

Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation

1 code implementation29 Nov 2023 Or Hirschorn, Shai Avidan

This approach not only enables object pose generation based on arbitrary keypoint definitions but also significantly reduces the associated costs, paving the way for versatile and adaptable pose estimation applications.

Animal Pose Estimation Category-Agnostic Pose Estimation +3

Securing Neural Networks with Knapsack Optimization

1 code implementation20 Apr 2023 Yakir Gorski, Amir Jevnisek, Shai Avidan

In this paper, we focus on ResNets, which serve as the backbone for many Computer Vision tasks, and we aim to reduce their non-linear components, specifically, the number of ReLUs.

Semantic Segmentation

Prior based Sampling for Adaptive LiDAR

1 code implementation14 Apr 2023 Amit Shomer, Shai Avidan

We propose SampleDepth, a Convolutional Neural Network (CNN), that is suited for an adaptive LiDAR.

Depth Completion

Taming Normalizing Flows

2 code implementations29 Nov 2022 Shimon Malnick, Shai Avidan, Ohad Fried

We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category.

SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow

1 code implementation CVPR 2023 Itai Lang, Dror Aiger, Forrester Cole, Shai Avidan, Michael Rubinstein

Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations.

regression Scene Flow Estimation

SAGA: Spectral Adversarial Geometric Attack on 3D Meshes

1 code implementation ICCV 2023 Tomer Stolik, Itai Lang, Shai Avidan

In this setting, an adversarial input mesh deceives the autoencoder by forcing it to reconstruct a different geometric shape at its output.

Adversarial Attack

Normalizing Flows for Human Pose Anomaly Detection

1 code implementation ICCV 2023 Or Hirschorn, Shai Avidan

Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more.

 Ranked #1 on Anomaly Detection on UBnormal (using extra training data)

Abnormal Event Detection In Video Anomaly Detection In Surveillance Videos +3

Aggregating Layers for Deepfake Detection

1 code implementation11 Oct 2022 Amir Jevnisek, Shai Avidan

Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network.

Binary Classification DeepFake Detection +2

Stress-Testing Point Cloud Registration on Automotive LiDAR

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

Rigid Point Cloud Registration (PCR) algorithms aim to estimate the 6-DOF relative motion between two point clouds, which is important in various fields, including autonomous driving.

Autonomous Driving Benchmarking +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 Vocal Bursts Valence Prediction

Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model

1 code implementation21 Nov 2021 Alon Zolfi, Shai Avidan, Yuval Elovici, Asaf Shabtai

In our experiments, we examined the transferability of our adversarial mask to a wide range of FR model architectures and datasets.

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.

Retrieval

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%.

BIG-bench Machine Learning Privacy Preserving

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.

Efficient Neural Network

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.

Generative Adversarial Network Image Generation +3

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 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.

Segmentation

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

Cannot find the paper you are looking for? You can Submit a new open access paper.