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Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.pdf
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🔗 Source: openaccess.thecvf.com
📊 Size: 2.15 MB
👤 Author: Hui Lv; Zhongqi Yue; Qianru Sun; Bin Luo; Zhen Cui; Hanwang Zhang
⬇️ Downloads: 128

Summary

Unbiased Multiple Instance Learning (UMIL) is proposed for Weakly Supervised Video Anomaly Detection (WSVAD), where video-level binary labels are available. UMIL aims to learn unbiased anomaly features by removing variant context biases, which is achieved by dividing samples into confident and ambiguous sets and seeking invariant features across them. This approach addresses the issue of Multiple Instance Learning (MIL) being biased towards simple context shortcuts, leading to false alarms and missing anomalies with different patterns. Experiments on UCF-Crime and TAD benchmarks demonstrate the effectiveness of UMIL.

Description

Unbiased Multiple Instance Learning (UMIL) is proposed for Weakly Supervised Video Anomaly Detection (WSVAD), where video-level binary labels are available.

Technical Information

  • File Format: PDF
  • File Size: 2.15 MB
  • Pages: 10
  • Language: EN
  • Author: Hui Lv; Zhongqi Yue; Qianru Sun; Bin Luo; Zhen Cui; Hanwang Zhang
  • Total Downloads: 128
  • Last Updated: 1 week ago

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