2 citations · 4 across the 9 of their papers we have counts for
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AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding
Yuzhou Long, Haodong Zhang, Yunpeng Yang +2
Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and offer little control over t…
Rethinking Auxiliary Modalities in Multimodal Zero-shot Anomaly Detection: From Semantic Fusion to Conditional Modulation
Peng Wu, Xin Ge, Yujia Sun +1
Recent foundation model-based methods have endowed RGB images with strong zero-shot anomaly detection (ZSAD) through vision-language pretraining. However, RGB observations alone re…
Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets
Peng Wu, Yuting Yan, Guansong Pang +4
Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands of video anomaly detection (VAD…
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
Qingsen Yan, Kangbiao Shi, Yixu Feng +4
Low-Light Image Enhancement (LLIE) aims to restore vivid content and details from corrupted low-light images. However, existing standard RGB (sRGB) color space-based LLIE methods o…
AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection
Peng Wu, Wanshun Su, Guansong Pang +4
With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-based detection approaches often struggle with information insuffic…
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
Zongcan Ding, Haodong Zhang, Peng Wu +4
Video anomaly detection (VAD) aims to identify unexpected events in videos and has wide applications in safety-critical domains. While semi-supervised methods trained on only norma…
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis
Zhiwei Yang, Chen Gao, Jing Liu +3
The rapid advancements in large language models (LLMs) have spurred growing interest in LLM-based video anomaly detection (VAD). However, existing approaches predominantly focus on…
HVI: A New Color Space for Low-light Image Enhancement
Qingsen Yan, Yixu Feng, Cheng Zhang +6
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods ar…
Deep Learning for Video Anomaly Detection: A Review
Peng Wu, Chengyu Pan, Yuting Yan +3
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 witnesse…
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts
Peng Wu, Xuerong Zhou, Guansong Pang +4
Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing wo…