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DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection

arXiv 2507.20629 ↗First posted 28 Jul 2025cs.CVcited by 0

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Abstract (arXiv)

The goal of video anomaly detection is tantamount to performing spatio-temporal localization of abnormal events in the video. The multiscale temporal dependencies, visual-semantic heterogeneity, and the scarcity of labeled data exhibited by video anomalies collectively present a challenging research problem in computer vision. This study offers a dual-path architecture called the Dual-Branch Adaptive Multiscale Spatiotemporal Framework (DAMS), which is based on multilevel feature decoupling and fusion, enabling efficient anomaly detection modeling by integrating hierarchical feature learning and complementary information. The main processing path of this framework integrates the Adaptive Multiscale Time Pyramid Network (AMTPN) with the Convolutional Block Attention Mechanism (CBAM). AMTPN enables multigrained representation and dynamically weighted reconstruction of temporal features through a three-level cascade structure (time pyramid pooling, adaptive feature fusion, and temporal context enhancement). CBAM maximizes the entropy distribution of feature channels and spatial dimensions through dual attention mapping. Simultaneously, the parallel path driven by CLIP introduces a contrastive language-visual pre-training paradigm. Cross-modal semantic alignment and a multiscale instance selection mechanism provide high-order semantic guidance for spatio-temporal features. This creates a complete inference chain from the underlying spatio-temporal features to high-level semantic concepts. The orthogonal complementarity of the two paths and the information fusion mechanism jointly construct a comprehensive representation and identification capability for anomalous events. Extensive experimental results on the UCF-Crime and XD-Violence benchmarks establish the effectiveness of the DAMS framework.

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References · 34

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22 more
  1. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 138
  2. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 123
  3. 2023Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Modelscited by 104
  4. 2024IMAGDressing-v1: Customizable Virtual Dressingcited by 100
  5. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 90
  6. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 89
  7. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  8. 2021GiT: Graph Interactive Transformer for Vehicle Re-identificationcited by 49
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  10. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
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  12. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  13. 2024LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Networkcited by 27
  14. 2025Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thoughtcited by 24
  15. 2025VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuningcited by 24
  16. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15
  17. 2024Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detectioncited by 10
  18. 2025Track Any Anomalous Object: A Granular Video Anomaly Detection Pipelinecited by 8
  19. 2025Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detectioncited by 7
  20. 2025SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Modelscited by 4
  21. 2025Video Anomaly Detection with Semantics-Aware Information Bottleneckcited by 2
  22. 2024Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detectioncited by 1

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