21 citations · 52 across the 10 of their papers we have counts for
11 papers
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…
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…
From Zero to Hero: Cold-Start Anomaly Detection
Tal Reiss, George Kour, Naama Zwerdling +2
When first deploying an anomaly detection system, e.g., to detect out-of-scope queries in chatbots, there are no observed data, making data-driven approaches ineffective. Zero-shot…
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…
Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
Oren Barkan, Tal Reiss, Jonathan Weill +4
Visual similarities discovery (VSD) is an important task with broad e-commerce applications. Given an image of a certain object, the goal of VSD is to retrieve images of different…
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Tal Reiss, Niv Cohen, Yedid Hoshen
Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anom…
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…
Anomaly Detection Requires Better Representations
Tal Reiss, Niv Cohen, Eliahu Horwitz +2
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during tr…
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…
Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment
Guy Amir, Ayala Prusak, Tal Reiss +2
Using multiple monitors is commonly thought to improve productivity, but this is hard to check experimentally. We use a survey, taken by 101 practitioners of which 80% have coded p…
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…