VigilFormer: Deformable Attention for Video Anomaly Detection with Causal Risk Inference

📰 ArXiv cs.AI

Learn how VigilFormer uses deformable attention and causal risk inference for video anomaly detection, improving accuracy and real-time throughput

advanced Published 16 Jun 2026

Action Steps

Implement VigilFormer using PyTorch or TensorFlow to detect anomalies in surveillance videos
Apply deformable spatio-temporal attention to extract features from video frames
Use causal temporal modeling to analyze temporal relationships between frames
Configure the model to balance detection accuracy and real-time throughput
Test the model on untrimmed surveillance video datasets

Who Needs to Know This

Computer vision engineers and researchers working on surveillance video analysis can benefit from this framework to improve anomaly detection accuracy and efficiency

Key Insight

💡 VigilFormer combines deformable attention and causal temporal modeling to improve video anomaly detection accuracy and efficiency

Key Takeaways

Learn how VigilFormer uses deformable attention and causal risk inference for video anomaly detection, improving accuracy and real-time throughput

Full Article

Title: VigilFormer: Deformable Attention for Video Anomaly Detection with Causal Risk Inference

Abstract:
arXiv:2606.14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both. We present VigilFormer, a unified framework that combines deformable spatio-temporal attention with causal temporal modeling to detect anomalies in untrimmed surveillance video. The proposed Deformable Spat

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