Papers Weakly-supervised Video Anomaly Detection
“Weakly-supervised Video Anomaly Detection” 태그가 달린 논문 41편 · 필터 해제
CADE: Continual Weakly-supervised Video Anomaly Detection with Ensembles
Video anomaly detection (VAD) has long been studied as a crucial problem in public security and crime prevention. In recent years, weakly-supervised VAD (WVAD) have attracted considerable attention due to their easy anno…
Weakly-supervised Video Anomaly DetectionContinual LearningRefineVAD: Semantic-Guided Feature Recalibration for Weakly Supervised Video Anomaly Detection
Weakly-Supervised Video Anomaly Detection aims to identify anomalous events using only video-level labels, balancing annotation efficiency with practical applicability. However, existing methods often oversimplify the an…
Weakly-supervised Video Anomaly DetectionLearning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment
Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight an…
Weakly-supervised Video Anomaly DetectionMultiple Instance LearningContrastive LearningMixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD) paradigm, only video-level labels are p…
Weakly-supervised Video Anomaly DetectionGV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection
Video anomaly detection (VAD) plays a critical role in public safety applications such as intelligent surveillance. However, the rarity, unpredictability, and high annotation cost of real-world anomalies make it difficul…
Weakly-supervised Video Anomaly DetectionVideo GenerationDual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level annota…
Anomaly DetectionAnomaly Detection In Surveillance VideosEdge-computingFederated Learning+5Just Dance with $π$! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact tha…
Anomaly DetectionOptical Flow EstimationVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling t…
Anomaly Detection In Surveillance VideosContrastive LearningMultiple Instance LearningSupervised Anomaly Detection+3UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks
Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model's anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently,…
Anomaly DetectionOptical Flow EstimationVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionJust Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact…
Anomaly DetectionOptical Flow EstimationVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionCross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection
Recently, weakly supervised video anomaly detection (WS-VAD) has emerged as a contemporary research direction to identify anomaly events like violence and nudity in videos using only video-level labels. However, this tas…
Anomaly DetectionGraph AttentionVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionInjecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems
Weakly Supervised Monitoring Anomaly Detection (WSMAD) utilizes weak supervision learning to identify anomalies, a critical task for smart city monitoring. However, existing multimodal approaches often fail to meet the r…
Anomaly DetectionBinary ClassificationClassificationContrastive Learning+6Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection
Weakly supervised video anomaly detection (WS-VAD) is a crucial area in computer vision for developing intelligent surveillance systems. This system uses three feature streams: RGB video, optical flow, and audio signals,…
Anomaly DetectionOptical Flow EstimationVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionWeakly 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 works typically involve extracting global …
Anomaly DetectionEvent Detectionobject-detectionObject Detection+2Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection
Video anomaly detection aims to develop automated models capable of identifying abnormal events in surveillance videos. The benchmark setup for this task is extremely challenging due to: i) the limited size of the traini…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionText Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection
Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-grained pseudo-labels based on weak-label and then self-training a classifier is currently a promising solution. However, since the…
Anomaly DetectionDomain AdaptationPseudo LabelVideo Anomaly Detection+1Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
Video Anomaly Detection (VAD) has been extensively studied under the settings of One-Class Classification (OCC) and Weakly-Supervised learning (WS), which however both require laborious human-annotated normal/abnormal la…
Anomaly DetectionOne-Class ClassificationVideo Anomaly DetectionWeakly-supervised Learning+1Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level anomalies using only video-level labels in training. Due to the limitation of coarse-grained labels Multi-Instance Learning (MIL) is prevail…
Anomaly DetectionMultiple Instance LearningVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionDynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection
The goal of weakly supervised video anomaly detection is to learn a detection model using only video-level labeled data. However, prior studies typically divide videos into fixed-length segments without considering the c…
Anomaly DetectionVideo Anomaly DetectionWeakly-supervised Video Anomaly DetectionBatchNorm-based Weakly Supervised Video Anomaly Detection
In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises from the inherent ambiguity in temporal…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionWeakly-supervised Video Anomaly Detection