Search Results for author:
Found 13 papers, 8 papers with code
Set Features for Fine-grained Anomaly Detection
Fine-grained anomaly detection has recently been dominated by segmentation based approaches.
Ranked #1 on
Anomaly Detection
on UEA time-series datasets
Improving Zero-Shot Models with Label Distribution Priors
We propose a new approach, CLIPPR (CLIP with Priors), which adapts zero-shot models for regression and classification on unlabelled datasets.
Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry.
Ranked #1 on
Anomaly Detection
on ODDS
3D Anomaly Detection and Segmentation
Representation Learning
+1
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute from being considered as relevant for anomaly detection.
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts.
Approaches Toward Physical and General Video Anomaly Detection
The classes differ in the presented phenomena, the normal class variability, and the kind of anomalies in the videos.
Ranked #1 on
Physical Video Anomaly Detection
on PHANTOM
(using extra training data)
Out-of-Distribution Detection Without Class Labels
Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm.
Ranked #1 on
Anomaly Detection
on Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102
(using extra training data)
Language-Guided Image Clustering
In this setting, the model is provided with an exhaustive list of phrases describing all the possible values of a specific attribute, together with a shared image-language embedding (e. g.
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data.
Dataset Summarization by K Principal Concepts
The output of our method is a set of K principal concepts that summarize the dataset.
Ranked #1 on
Image Clustering
on ImageNet-100
(using extra training data)
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning.
Ranked #1 on
Anomaly Detection
on Cats-and-Dogs
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images.
Ranked #10 on
Unsupervised Anomaly Detection
on DAGM2007
(using extra training data)
Deep Nearest Neighbor Anomaly Detection
Nearest neighbors is a successful and long-standing technique for anomaly detection.
Ranked #2 on
Anomaly Detection
on Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200
(using extra training data)
