6 papers · 1 filter
WISE: Weighted Iterative Society-of-Experts for Robust Multimodal Multi-Agent Debate
Anoop Cherian, River Doyle, Eyal Ben-Dov +2
Recent large language models (LLMs) are trained on diverse corpora and tasks, leading them to develop complementary strengths. Multi-agent debate (MAD) has emerged as a popular way…
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…
MMHOI: Modeling Complex 3D Multi-Human Multi-Object Interactions
Kaen Kogashi, Anoop Cherian, Meng-Yu Jennifer Kuo
Real-world scenes often feature multiple humans interacting with multiple objects in ways that are causal, goal-oriented, or cooperative. Yet existing 3D human-object interaction (…
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…
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…