5 papers
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…
Agentic AI-Empowered Dynamic Survey Framework
Furkan Mumcu, Lokman Bekit, Michael J. Jones +2
Survey papers play a central role in synthesizing and organizing scientific knowledge, yet they are increasingly strained by the rapid growth of research output. As new work contin…
LLM-Guided Agentic Object Detection for Open-World Understanding
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Object detection traditionally relies on fixed category sets, requiring costly re-training to handle novel objects. While Open-World and Open-Vocabulary Object Detection (OWOD and…
Improving Open-World Object Localization by Discovering Background
Ashish Singh, Michael J. Jones, Kuan-Chuan Peng +3
Our work addresses the problem of learning to localize objects in an open-world setting, i.e., given the bounding box information of a limited number of object classes during train…
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…