327 citations · 726 across the 29 of their papers we have counts for
20 papers · 1 filter
Progressive Photorealistic Simplification
Adi Rosenthal, Dana Berman, Yedid Hoshen +1
Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or paintings. While effective…
Dreamix: Video Diffusion Models are General Video Editors
Eyal Molad, Eliahu Horwitz, Dani Valevski +5
Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very…
Conffusion: Confidence Intervals for Diffusion Models
Eliahu Horwitz, Yedid Hoshen
Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting. Current diffusion…
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Niv Cohen, Jonathan Kahana, Yedid Hoshen
Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.…
A Contrastive Objective for Learning Disentangled Representations
Jonathan Kahana, Yedid Hoshen
Learning representations of images that are invariant to sensitive or unwanted attributes is important for many tasks including bias removal and cross domain retrieval. Here, our o…
Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples
Nir Zabari, Yedid Hoshen
Semantic segmentation is a key computer vision task that has been actively researched for decades. In recent years, supervised methods have reached unprecedented accuracy, however…
Out-of-Distribution Detection Without Class Labels
Niv Cohen, Ron Abutbul, Yedid Hoshen
Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the n…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Aviv Gabbay, Niv Cohen, Yedid Hoshen
Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data. As an alternative approach, recent methods rely on l…
Mean-Shifted Contrastive Loss for Anomaly Detection
Tal Reiss, Yedid Hoshen
Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dat…
Scaling-up Disentanglement for Image Translation
Aviv Gabbay, Yedid Hoshen
Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes i…
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Tal Reiss, Niv Cohen, Liron Bergman +1
Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-superv…
Improving Style-Content Disentanglement in Image-to-Image Translation
Aviv Gabbay, Yedid Hoshen
Unsupervised image-to-image translation methods have achieved tremendous success in recent years. However, it can be easily observed that their models contain significant entanglem…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Niv Cohen, Yedid Hoshen
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is t…
Training End-to-end Single Image Generators without GANs
Yael Vinker, Nir Zabari, Yedid Hoshen
We present AugurOne, a novel approach for training single image generative models. Our approach trains an upscaling neural network using non-affine augmentations of the (single) in…
Style Generator Inversion for Image Enhancement and Animation
Aviv Gabbay, Yedid Hoshen
One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GAN…
Unsupervised Correlation Analysis
Yedid Hoshen, Lior Wolf
Linking between two data sources is a basic building block in numerous computer vision problems. In this paper, we set to answer a fundamental cognitive question: are prior corresp…
Live Video Synopsis for Multiple Cameras
Yedid Hoshen, Shmuel Peleg
Video surveillance cameras generate most of recorded video, and there is far more recorded video than operators can watch. Much progress has recently been made using summarization…
An Egocentric Look at Video Photographer Identity
Yedid Hoshen, Shmuel Peleg
Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substanti…