327 citations · 466 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
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…
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.…
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
Niv Cohen, Rinon Gal, Eli A. Meirom +2
Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for r…