1 citations · 1 across the 13 of their papers we have counts for
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Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scenes. While this focus has led…
Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detection
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Existing semi-supervised video anomaly detection (VAD) methods often struggle with detecting complex anomalies involving object interactions and generally lack explainability. To o…
MLLM-Guided Autonomous Object Detection for Open-World Understanding
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Object detection typically relies on predefined category vocabularies, while open-world and open-vocabulary approaches remain limited by generic unknown labels or externally specif…
ComplexVAD: Detecting Interaction Anomalies in Video
Furkan Mumcu, Michael J. Jones, Yasin Yilmaz +1
Existing video anomaly detection datasets are inadequate for representing complex anomalies that occur due to the interactions between objects. The absence of complex anomalies in…
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems
Furkan Mumcu, Keval Doshi, Yasin Yilmaz
Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understa…