Chao Huang

Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection

Multimodal Large Language Models (MLLMs) provide a natural way to make video anomaly detection more explainable. However, their final decisions do not always fully use the discriminative information contained in their hidden states, an issue we refer to as representation--behavior misalignment. We decompose this gap in...

RoMod: Temporal Routing Modulation via Mixture-of-Experts for Video Anomaly Detection

RoMod is proposed, an efficient VAD framework trained with only \(5\%\) of weakly labeled videos that achieves state-of-the-art performance while running substantially faster than dense backbones of comparable size.

IN3SIGHT: Towards Cognitive Forensic Reasoning for OOC Misinformation Detection.

Out-of-context (OOC) misinformation, where genuine images are paired with misleading text, poses substantial societal risks due to its deceptive narratives. Prior efforts primarily assess image-text consistency but often lack explainable judgments, limiting their usefulness for forensic validation. Recently, Multimodal...

Semantic Invariant Representation Learning and Data-Driven Label Prototype Modeling for Incomplete Multi-view Multi-label Classification