5 citations · 5 across the 5 of their papers we have counts for
7 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…
SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere
Chao Huang, Pengfei Wei, Penfei Wei +6
Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods universally depend on large…
M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection
Chao Huang, Yanhui Li, Yunkang Cao +5
Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable…
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…
Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
Hao Li, Wei Wang, Cong Wang +4
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.…
Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
Yunxin Li, Zhenyu Liu, Baotian Hu +4
Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual i…