3 citations · 3 across the 2 of their papers we have counts for
5 papers
RoMod: Temporal Routing Modulation via Mixture-of-Experts for Video Anomaly Detection
Chao Huang, Pengfei Wei, Benfeng Wang +5
Intermediate-layer features from multimodal large language models have shown strong potential for video anomaly detection (VAD), yet the origin of their discriminative power remain…
Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection
Chao Huang, Pengfei Wei, Kaige Li +4
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 discrimi…
Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method
Chao Huang, Benfeng Wang, Wei Wang +5
Recent progress in reasoning capabilities of Multimodal Large Language Models(MLLMs) has highlighted their potential for performing complex video understanding tasks. However, in t…
Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought
Chao Huang, Benfeng Wang, Jie Wen +4
Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based…
A Semi-supervised Physics-Aware Triple-Stream Underwater Image Enhancement Network
Shixuan Xu, Hao Qi, Wei Wang +4
Underwater images normally suffer from degradation due to the transmission medium of water bodies. Both traditional prior-based approaches and deep learning-based methods have been…