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

• URL: http://arxiv.org/abs/2606.14724v1
• Date: Sun, 31 May 2026 14:27:57 GMT
• Title: VigilFormer: Deformable Attention for Video Anomaly Detection with Causal Risk Inference
• Abstract summary: We present a unified framework that combines deformable attention with causal temporal modeling to detect anomalies in video surveillance footage.<n>We evaluate our framework on VigilFormer, ShanghaiTech, and CUHK Avenue, at 41.5 FPS on a single GPU, recently outperforming weakly-supervised methods in both accuracy and speed.
• Score: 2.538209532048867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Spatio-Temporal Encoder (DSTE) attends to a sparse set of informative locations across frames, avoiding the quadratic cost of dense attention while retaining the ability to capture irregular motion patterns. A Causal Anomaly Classifier (CAC) applies dilated causal convolutions over snippet-level features and optimizes a contrastive multiple-instance learning objective that separates anomalous and normal representations without frame-level labels. To meet deployment constraints, an Adaptive Confidence Scheduler (ACS) dynamically skips low-information frames at inference time, reducing redundant computation in static scenes. Evaluated on UCF-Crime, ShanghaiTech, and CUHK Avenue, VigilFormer achieves AUC scores of 87.83%, 97.21%, and 89.74% respectively, at 41.5 FPS on a single GPU, outperforming recent weakly-supervised methods in both accuracy and speed.

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