Search Results for author:
Found 9 papers, 7 papers with code
Real-Time Deepfake Detection in the Real-World
The image deepfake score is the pooled score of its patches.
From Zero to Hero: Cold-Start Anomaly Detection
This paper studies the realistic but underexplored cold-start setting where an anomaly detection model is initialized using zero-shot guidance, but subsequently receives a small number of contaminated observations (namely, that may include anomalies).
Ranked #1 on
Cold-Start Anomaly Detection
on BANKING77-OOS
Detecting Deepfakes Without Seeing Any
We therefore introduce the concept of "fact checking", adapted from fake news detection, for detecting zero-day deepfake attacks.
Ranked #1 on
DeepFake Detection
on FakeAVCeleb
Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
Given an image of a certain object, the goal of VSD is to retrieve images of different objects with high perceptual visual similarity.
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
It is tempting to hypothesize that anomaly detection can improve indefinitely by increasing the scale of our networks, making their representations more expressive.
Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection
Surprisingly, we find that this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the largest and most complex VAD dataset.
Ranked #1 on
Abnormal Event Detection In Video
on UCSD Ped2
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
Mean-Shifted Contrastive Loss for Anomaly Detection
We take the approach of transferring representations pre-trained on external datasets for anomaly detection.
Ranked #4 on
Anomaly Detection
on One-class CIFAR-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
