Weakly Supervised Video Anomaly Detection

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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RoMod: Temporal Routing Modulation via Mixture-of-Experts for Video Anomaly Detection
Oct 4, 2026Chao Huang, Pengfei Wei, Benfeng Wang +5Video Anomaly Detection Multimodal Foundation Models
Sun Yat-sen University · University of Macau
Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection
Oct 4, 2026Chao Huang, Pengfei Wei, Kaige Li +4Interpretable Anomaly Detection Representation Alignment
Sun Yat-sen University · University of Macau
Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
Sep 7, 2026Sébastien Thuau, Amira Gran, Siba Haidar +1Multimodal Anomaly Detection Weakly Supervised Video Anomaly Detection
esieaLab, ESIEA, Paris, France · ETIS Laboratory, CNRS, UMR8051, CY Cergy Paris University, Paris, France
Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection
Aug 12, 2026Sara Abdulaziz, Abdulrahman Al-Abri, Giacomo D'Amicantonio +1Anomaly Localization Weakly Supervised Video Anomaly Detection
Eindhoven University of Technology, 5612 AE Eindhoven, Netherlands
MuST-VAD: Mutual Structured Learning for Video Anomaly Detection
Aug 7, 2026Satoshi Hashimoto, Hitoshi Nishimura, Mori KurokawaVLM Adaptation Weakly Supervised Video Anomaly Detection
KDDI Research, Inc. Fujimino, Saitama, Japan
FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition
Aug 7, 2026Ghani Haider, Majid Kundroo, Boyun Eom +3Prototype-Based Learning Non-IID Federated Learning
School of Information and Communication Engineering, Chungbuk National University, Cheongju, 28644, Republic of Korea · Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, Republic of Korea · Institute of Artificial Intelligence (IAI), University of Central Florida, Orlando, 32816, USA
Structured Evidence Selection for Weakly Supervised Video Anomaly Detection
Jul 11, 2026Chenglizhao Chen, Tianxiang Nan, Wen Li +4Anomaly Localization Weakly Supervised Video Anomaly Detection
China University of Petroleum (East China) Qingdao, China
Learning to Watch: Active Video Anomaly Understanding via Interleaved Policy Optimization
Jul 1, 2026Mengjingcheng Mo, Jiaxu Leng, Xinbo GaoVideo Understanding Direct Preference Optimization
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · Chongqing College of Artificial Intelligence, Chongqing, China
Learning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection
Jun 28, 2026Hamza Karim, Nghia Nguyen, Lokman Bekit +1Anomaly Localization Video Anomaly Detection
University of South Florida
VigilFormer: Deformable Attention for Video Anomaly Detection with Causal Risk Inference
May 31, 2026Xinze ZhangTransformer Attention Deformable Attention
University of Southern California, Los Angeles, CA 90007, USA
Weakly-Supervised Spatiotemporal Anomaly Detection
May 13, 2026Urvi Gianchandani, Praveen Tirupattur, Mubarak ShahAnomaly Localization Weakly Supervised Video Anomaly Detection
University of Texas at Dallas · University of Central Florida
Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models
May 12, 2026Furkan Mumcu, Michael J. Jones, Anoop Cherian +1Large Vision-Language Models Anomaly Localization
University of South Florida · Mitsubishi Electric Research Laboratories (MERL)
Auditing Frame-Level AUC in Weakly Supervised Video Anomaly Detection: Granularity, Resolution, and Scene Bias
Date pendingSara Abdulaziz, Egor BondarevAnomaly Localization Algorithmic Bias
Eindhoven University of Technology, 5612 AE Eindhoven, Netherlands

Oct 4, 2026, cs.CVJ/K move · Enter open · S save

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

Chao Huang, Pengfei Wei, Benfeng Wang, Chengliang Liu+4

Sun Yat-sen University · University of Macau

Video Anomaly Detection Multimodal Foundation Models Mixture-of-Experts Models Weakly Supervised Video Anomaly Detection

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Intermediate-layer features from multimodal large language models have shown strong potential for video anomaly detection (VAD), yet the origin of their discriminative power remains unclear. We study this question using sparse mixture-of-experts (MoE) models, whose explicit expert structure and sparse activation make their internal computation easier to inspect. With a fully frozen backbone and no additional training, we find that anomaly-related evidence is concentrated in a small set of experts. These experts recur across layers, spontaneously specialize in different anomaly types, and together form a dynamic routing subnetwork. We further show that the output channels most strongly influenced by these experts are also the hidden dimensions that contain the most anomaly-relevant information. Routing statistics can therefore serve as an internal anomaly cue that complements semantic features.Based on these findings, we propose RoMod, an efficient VAD framework trained with only

5\%

of weakly labeled videos. RoMod includes a Routing-Modulated Fusion module, RoMF, and a Routing-aware Temporal Network, RoTN. RoMF uses routing signals to adaptively recalibrate hidden semantic channels. Its design also prevents the routing branch from bypassing semantic features and making predictions on its own. RoTN captures the temporal evolution of anomalies from onset to persistence and termination. Experiments on three benchmarks show that RoMod achieves state-of-the-art performance while running substantially faster than dense backbones of comparable size.

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