5 papers
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…