Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts
This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …
single detection model that can generalize to detect anomalies in diverse datasets from …
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …
learning systems and has shaped the field of OOD detection. Meanwhile, several other …
Weakly supervised video anomaly detection and localization with spatio-temporal prompts
Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-
level anomalous event detection with only coarse video-level annotations available. Existing …
level anomalous event detection with only coarse video-level annotations available. Existing …
Deep Learning for Video Anomaly Detection: A Review
Video anomaly detection (VAD) aims to discover behaviors or events deviating from the
normality in videos. As a long-standing task in the field of computer vision, VAD has …
normality in videos. As a long-standing task in the field of computer vision, VAD has …
Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure
As vision-language models like CLIP are widely applied to zero-shot tasks and gain
remarkable performance on in-distribution (ID) data, detecting and rejecting out-of …
remarkable performance on in-distribution (ID) data, detecting and rejecting out-of …
Deep Graph Anomaly Detection: A Survey and New Perspectives
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit
biased detection when faced with challenging or unseen events and lack interpretability. To …
biased detection when faced with challenging or unseen events and lack interpretability. To …
Hawk: Learning to Understand Open-World Video Anomalies
Video Anomaly Detection (VAD) systems can autonomously monitor and identify
disturbances, reducing the need for manual labor and associated costs. However, current …
disturbances, reducing the need for manual labor and associated costs. However, current …
Networking Systems for Video Anomaly Detection: A Tutorial and Survey
The increasing prevalence of surveillance cameras in smart cities, coupled with the surge of
online video applications, has heightened concerns regarding public security and privacy …
online video applications, has heightened concerns regarding public security and privacy …
Video Anomaly Detection via Progressive Learning of Multiple Proxy Tasks
Learning multiple proxy tasks is a popular training strategy in semi-supervised video
anomaly detection. However, the traditional method of learning multiple proxy tasks …
anomaly detection. However, the traditional method of learning multiple proxy tasks …