3 papers
cs.CV2026
SENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Driving
Nghia T. Nguyen, Lokman Bekit, Yasin Yilmaz
Autonomous vehicles (AVs) must navigate not only motion-based hazards but also socially complex situations whose danger is constituted by inter-agent relationships rather than move…
cs.CV2026
Learning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection
Hamza Karim, Nghia Nguyen, Lokman Bekit +1
Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neglecting their spatial extent…
cs.CV2026
QVAD: A Question-Centric Agentic Framework for Efficient and Training-Free Video Anomaly Detection
Lokman Bekit, Hamza Karim, Nghia T Nguyen +1
Video Anomaly Detection (VAD) is a fundamental challenge in computer vision, particularly due to the open-set nature of anomalies. While recent training-free approaches utilizing V…