What question did this study set out to answer?

To develop a weakly supervised video anomaly detection framework that captures subtle anomalous clues and models multi-scale temporal dependencies without over-relying on dominant salient segments.

August 20, 2026International Journal of Pattern Recognition and Artificial Intelligence

TASG-VAD: Weakly Supervised Video Anomaly Detection via Temporal Variation Attention and Adaptive Saliency Guidance

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Authors

LPLihu PanTaiyuan University of Science and Technology MHMingkai Hu LZLinliang ZhangShanxi Transportation Research Institute

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Implication

Computational study demonstrates improved video anomaly detection using dual-scale temporal modeling, indicating enhanced detection of subtle events in surveillance footage.

Key Points

To develop a weakly supervised video anomaly detection framework that captures subtle anomalous clues and models multi-scale temporal dependencies without over-relying on dominant salient segments.
Designed a Temporal Variation Attention (TVA) mechanism using second-order temporal differences to highlight dynamic anomalies over static backgrounds.
Constructed a Dual-Scale Temporal Encoder (DSTE) combining dual-branch convolutions and parameter-free attention for local and global context modeling.
Implemented an Adaptive Saliency Guidance (ASG) strategy featuring intra-video saliency ranking and dynamic masking to expose subtle anomalies.
Achieved an AUC of 88.21% on the UCF-Crime dataset and 98.38% on the ShanghaiTech dataset.
Reached an average precision (AP) of 84.60% on the XD-Violence benchmark.
Maintained low computational overhead and fast inference using approximately 1.7M parameters.

Cite This Study

Pan et al. (2026) studied this question.

synapsesocial.com/papers/6a86b5978a91293e6a1cd02e https://doi.org/10.1142/s0218001426520208

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August 20, 2026International Journal of Pattern Recognition and Artificial Intelligence

TASG-VAD: Weakly Supervised Video Anomaly Detection via Temporal Variation Attention and Adaptive Saliency Guidance

View Full Paper

Ask AI

Bookmark

Authors

LPLihu PanTaiyuan University of Science and Technology MHMingkai Hu LZLinliang ZhangShanxi Transportation Research Institute

Discussion

Member takes

Implication

Computational study demonstrates improved video anomaly detection using dual-scale temporal modeling, indicating enhanced detection of subtle events in surveillance footage.

Key Points

To develop a weakly supervised video anomaly detection framework that captures subtle anomalous clues and models multi-scale temporal dependencies without over-relying on dominant salient segments.
Designed a Temporal Variation Attention (TVA) mechanism using second-order temporal differences to highlight dynamic anomalies over static backgrounds.
Constructed a Dual-Scale Temporal Encoder (DSTE) combining dual-branch convolutions and parameter-free attention for local and global context modeling.
Implemented an Adaptive Saliency Guidance (ASG) strategy featuring intra-video saliency ranking and dynamic masking to expose subtle anomalies.
Achieved an AUC of 88.21% on the UCF-Crime dataset and 98.38% on the ShanghaiTech dataset.
Reached an average precision (AP) of 84.60% on the XD-Violence benchmark.
Maintained low computational overhead and fast inference using approximately 1.7M parameters.

Cite This Study

Pan et al. (2026) studied this question.

synapsesocial.com/papers/6a86b5978a91293e6a1cd02e https://doi.org/10.1142/s0218001426520208

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