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06/12/2023
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Anomaly detection methods, powered by deep learning, have recently been ...
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02/23/2023
Set Features for Fine-grained Anomaly Detection
Fine-grained anomaly detection has recently been dominated by segmentati...
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12/01/2022
Improving Zero-Shot Models with Label Distribution Priors
Labeling large image datasets with attributes such as facial age or obje...
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10/19/2022
Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in...
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07/07/2022
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Anomaly detection methods strive to discover patterns that differ from t...
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04/04/2022
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
Large Vision Language models pretrained on web-scale data provide re...
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12/14/2021
Out-of-Distribution Detection without Class Labels
Anomaly detection methods identify samples that deviate from the normal ...
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12/14/2021
Approaches Toward Physical and General Video Anomaly Detection
In recent years, many works have addressed the problem of finding never-...
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06/29/2021
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Unsupervised disentanglement has been shown to be theoretically impossib...
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04/08/2021
The Single-Noun Prior for Image Clustering
Self-supervised clustering methods have achieved increasing accuracy in ...
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10/12/2020
PANDA – Adapting Pretrained Features for Anomaly Detection
Anomaly detection methods require high-quality features. One way of obta...
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05/05/2020
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhib...
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02/24/2020
