704 citations · 3.6k across the 98 of their papers we have counts for
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On The Relationship Between Universal Adversarial Attacks And Sparse Representations
Dana Weitzner, Raja Giryes
The prominent success of neural networks, mainly in computer vision tasks, is increasingly shadowed by their sensitivity to small, barely perceivable adversarial perturbations in i…
AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
Tal Shaharabany, Aviad Dahan, Raja Giryes +1
The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. Howe…
Group Orthogonalization Regularization For Vision Models Adaptation and Robustness
Yoav Kurtz, Noga Bar, Raja Giryes
As neural networks become deeper, the redundancy within their parameters increases. This phenomenon has led to several methods that attempt to reduce the correlation between convol…
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Sivan Doveh, Assaf Arbelle, Sivan Harary +9
Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, vis…
UDPM: Upsampling Diffusion Probabilistic Models
Shady Abu-Hussein, Raja Giryes
Denoising Diffusion Probabilistic Models (DDPM) have recently gained significant attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds nois…
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Dina Bashkirova, Samarth Mishra, Diala Lteif +16
Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
Dana Cohen-Bar, Elad Richardson, Gal Metzer +2
Recent breakthroughs in text-guided image generation have led to remarkable progress in the field of 3D synthesis from text. By optimizing neural radiance fields (NeRF) directly fr…
TEXTure: Text-Guided Texturing of 3D Shapes
Elad Richardson, Gal Metzer, Yuval Alaluf +2
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion mode…
ADIR: Adaptive Diffusion for Image Reconstruction
Shady Abu-Hussein, Tom Tirer, Raja Giryes
Denoising diffusion models have recently achieved remarkable success in image generation, capturing rich information about natural image statistics. This makes them highly promisin…
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Gal Metzer, Elad Richardson, Or Patashnik +2
Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score dis…
MAEDAY: MAE for few and zero shot AnomalY-Detection
Eli Schwartz, Assaf Arbelle, Leonid Karlinsky +4
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to…
PIP: Positional-encoding Image Prior
Nimrod Shabtay, Eli Schwartz, Raja Giryes
In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e.g. noisy) image but in the process learns to reconstruct the clean…
Teaching Structured Vision&Language Concepts to Vision&Language Models
Sivan Doveh, Assaf Arbelle, Sivan Harary +8
Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a cha…
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images
Dominik J. E. Waibel, Ernst Röell, Bastian Rieck +2
Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the in…
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
Shahaf Ettedgui, Shady Abu-Hussein, Raja Giryes
Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, s…
Stress-Testing Point Cloud Registration on Automotive LiDAR
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 drivi…
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…
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest
Gilad Cohen, Raja Giryes
Although Deep Neural Networks (DNNs) achieve excellent performance on many real-world tasks, they are highly vulnerable to adversarial attacks. A leading defense against such attac…
FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction
Brian Gordon, Sigal Raab, Guy Azov +2
The increasing availability of video recordings made by multiple cameras has offered new means for mitigating occlusion and depth ambiguities in pose and motion reconstruction meth…
Detector-Free Weakly Supervised Grounding by Separation
Assaf Arbelle, Sivan Doveh, Amit Alfassy +14
Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…
Image Restoration by Deep Projected GSURE
Shady Abu-Hussein, Tom Tirer, Se Young Chun +2
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. In recent years, solutions that are based on deep Convolutional Ne…
ISP Distillation
Eli Schwartz, Alex Bronstein, Raja Giryes
Nowadays, many of the images captured are `observed' by machines only and not by humans, e.g., in autonomous systems. High-level machine vision models, such as object recognition o…
Fine-grained Angular Contrastive Learning with Coarse Labels
Guy Bukchin, Eli Schwartz, Kate Saenko +4
Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This…
Best Buddies Registration for Point Clouds
Amnon Drory, Tal Shomer, Shai Avidan +1
We propose new, and robust, loss functions for the point cloud registration problem. Our loss functions are inspired by the Best Buddies Similarity (BBS) measure that counts the nu…