Combining uncertainty modeling and temporal-channel network with CLIP model for weakly supervised video anomaly detection

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姓名

江毓晴(Yu-Qing Jiang) 查詢紙本館藏

畢業系所

軟體工程研究所

論文名稱

(Combining uncertainty modeling and temporal-channel network with CLIP model for weakly supervised video anomaly detection)

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至系統瀏覽論文 (2025-7-17以後開放)

摘要(中)

為了確保公共安全和保護個人財產，監視攝影機被廣泛設置在各種公共場所、公司以及住宅，用於記錄違法和異常活動。然而，異常事件通常只佔整部監視影片的一小部分。因此，影片異常檢測至關重要，因為它的目的是區分異常事件和正常事件，並找到這些異常發生的確切時間。近年來，視覺語言模型（VLM）在各種影像相關任務中取得了巨大成功。許多研究已將 VLM 的應用擴展到各種影片任務中，包括弱監督式影片異常檢測。我們將視覺語言模型與多尺度時序Transformer、通道注意力機制和不確定性建模策略結合，以捕捉更多判別性特徵並更有效地分離異常事件與正常事件。實驗結果表明，對於 UCF-Crime 和 XD-Violence 資料集中的大多數類別，我們的方法在弱監督式影片異常檢測方面優於目前最先進的模型。

摘要(英)

To ensure public safety and protect private property, surveillance cameras are widely deployed in various public spaces, companies, and residences to record illegal and anomalous activities. However, abnormal events typically account for only a small fraction of the total surveillance footage. Therefore, video anomaly detection is crucial, as it aims to distinguish abnormal events from normal events and find the exact time of these anomalies. In recent years, Vision-Language Models (VLMs) have achieved significant success in various image-related tasks. Many studies have extended the application of VLMs to video-level tasks, including weakly supervised video anomaly detection. We integrate VLM with multi-scale temporal transformer, channel attention mechanism, and uncertainty modeling strategy to capture more discriminative features and more effectively distinguish abnormal events from normal events. Experimental results show that our method outperforms current state-of-the-art models in weakly supervised video anomaly detection for most of the categories in the UCF-Crime and XD-Violence datasets.

關鍵字(中)

★ 弱監督式學習
★ 影片異常檢測

關鍵字(英)

★ weakly supervised learning
★ video anomaly detection

論文目次

1 Introduction 1
2 Related Work 4
2.1 Video anomaly detection 4
2.1.1 Semi supervised video anomaly detection 4
2.1.2 Weakly supervised video anomaly detection 5
2.2 Vision-Language Pre-training 6
3 Preliminary 8
3.1 CLIP 8
3.2 Multi-Instance Learning 9
3.3 VadCLIP 10
3.3.1 Local and Global Temporal Adapter 11
3.3.2 Dual Branch 12
3.4 Squeeze-and-Excitation Networks 13
3.5 Uncertainty Modeling 14
3.5.1 Uncertainty Modeling Loss 15
3.5.2 Background Entropy Loss 15
4 Design 17
4.1 Motivation 17
4.2 Problem Statement 17
4.3 Research Challenges 18
4.4 Proposed System Architecture 19
4.4.1 Preprocessing and Feature Extraction 20
4.4.2 Multi-Scale Local and Global Temporal Adapter 21
4.4.3 Loss Function 24
5 Performance 26
5.1 Datasets 26
5.2 Evaluation Metrics 27
5.3 Experimental Environment 28
5.4 Experimental Configurations 29
5.5 Experimental Results and Analysis 29
5.6 Ablation Studies 34
6 Conclusion 37

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指導教授

孫敏德(Min-Te Sun)

審核日期

2024-7-23

推文