21 citations · 56 across the 25 of their papers we have counts for
14 papers · 1 filter
Alterbute: Editing Intrinsic Attributes of Objects in Images
Tal Reiss, Daniel Winter, Matan Cohen +4
We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, material, and even the shape of an obje…
LightLab: Controlling Light Sources in Images with Diffusion Models
Nadav Magar, Amir Hertz, Eric Tabellion +4
We present a simple, yet effective diffusion-based method for fine-grained, parametric control over light sources in an image. Existing relighting methods either rely on multiple i…
ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation
Daniel Winter, Asaf Shul, Matan Cohen +4
This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specif…
Dataset Size Recovery from LoRA Weights
Mohammad Salama, Jonathan Kahana, Eliahu Horwitz +1
Model inversion and membership inference attacks aim to reconstruct and verify the data which a model was trained on. However, they are not guaranteed to find all training samples…
Real-Time Deepfake Detection in the Real-World
Bar Cavia, Eliahu Horwitz, Tal Reiss +1
Recent improvements in generative AI made synthesizing fake images easy; as they can be used to cause harm, it is crucial to develop accurate techniques to identify them. This pape…
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
Daniel Winter, Matan Cohen, Shlomi Fruchter +3
Diffusion models have revolutionized image editing but often generate images that violate physical laws, particularly the effects of objects on the scene, e.g., occlusions, shadows…
Distilling Datasets Into Less Than One Image
Asaf Shul, Eliahu Horwitz, Yedid Hoshen
Dataset distillation aims to compress a dataset into a much smaller one so that a model trained on the distilled dataset achieves high accuracy. Current methods frame this as maxim…
Detecting Deepfakes Without Seeing Any
Tal Reiss, Bar Cavia, Yedid Hoshen
Deepfake attacks, malicious manipulation of media containing people, are a serious concern for society. Conventional deepfake detection methods train supervised classifiers to dist…
Set Features for Anomaly Detection
Niv Cohen, Issar Tzachor, Yedid Hoshen
This paper proposes to use set features for detecting anomalies in samples that consist of unusual combinations of normal elements. Many leading methods discover anomalies by detec…
Set Features for Fine-grained Anomaly Detection
Niv Cohen, Issar Tzachor, Yedid Hoshen
Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as norma…
An Attribute-based Method for Video Anomaly Detection
Tal Reiss, Yedid Hoshen
Video anomaly detection (VAD) identifies suspicious events in videos, which is critical for crime prevention and homeland security. In this paper, we propose a simple but highly ef…
Improving Zero-Shot Models with Label Distribution Priors
Jonathan Kahana, Niv Cohen, Yedid Hoshen
Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate s…
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
Eliahu Horwitz, Yedid Hoshen
Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate…
Dataset Summarization by K Principal Concepts
Niv Cohen, Yedid Hoshen
We propose the new task of K principal concept identification for dataset summarizarion. The objective is to find a set of K concepts that best explain the variation within the dat…