Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection

arXiv:2412.03044 · cs.CV · Submitted 2024-12-04 · Read on arXiv

cs.CV

Submitted: 2024-12-04

Updated: 2026-10-11

Journal ref: IEEE Transactions on Image Processing, Early Access, 2026

DOI: 10.1109/TIP.2026.3730816

Project page: https://xiaofeng-tan.github.io/projects/FG-Diff

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 33× in this paper · explained in 5 episodes
• Diffusion Model — 26× in this paper · explained in 10 episodes
• Generator — 22× in this paper · explained in 2 episodes
• Reconstruction Error — 17× in this paper · explained in 2 episodes
• AUC — 16× in this paper · explained in 2 episodes
• Diffusion Models — 9× in this paper · explained in 9 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Generalization — 6× in this paper · explained in 13 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 3 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Loss Function — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Winter Conference on Applications of Computer Vision — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Adversarial Training — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Decoder — 2× in this paper · explained in 2 episodes
• Mode Collapse — 2× in this paper · explained in 2 episodes
• Normalizing Flow — 2× in this paper · explained in 3 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 3 episodes
• 3D Human Pose Estimation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 6 episodes
• Core — 1× in this paper · explained in 2 episodes
• D Discrete Cosine Transform — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Hybrid Approaches — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Image Processing — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• New Laboratory of Pattern Recognition — 1× in this paper
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Temporal Consistency — 1× in this paper · explained in 3 episodes

Sources

• DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space
• A Survey on Diffusion Models for Anomaly Detection

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