A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation

arXiv:2610.11149 · cs.CV, eess.IV · Submitted 2026-10-08 · Read on arXiv

cs.CV, eess.IV

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/open-mmlab/mmtracking

Terminology

• Transformer — 31× in this paper · explained in 2 episodes
• Score Function — 17× in this paper · explained in 4 episodes
• AUC — 12× in this paper · explained in 2 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Distillation — 7× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Self-distillation — 6× in this paper · explained in 6 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Latent Space — 5× in this paper · explained in 7 episodes
• Unified Framework — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Computational overhead — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Alignment Techniques — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• Score-Based Generative Modeling through Stochastic Differential Equations
• Multiscale Score Matching for Out-of-Distribution Detection
• Motion-Aware Feature for Improved Video Anomaly Detection
• Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
• Latte: Latent Diffusion Transformer for Video Generation
• Deep multi-scale video prediction beyond mean square error
• An Attribute-based Method for Video Anomaly Detection
• Adam: A Method for Stochastic Optimization
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models