Unbiased multiple instance learning for weakly supervised video anomaly detection

Unbiased multiple instance learning for weakly supervised video anomaly detection

Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippetlevel predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from man...

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Bibliographic Details
Main Authors: Lv, Hui, Yue, Zhongqi, Sun, Qianru, Luo, Bin, Cui, Zhen, Zhang, Hanwang
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Multiple Instance Learning
Weakly Supervised Video Anomaly Detection
Online Access:https://hdl.handle.net/10356/169509
https://cvpr2023.thecvf.com/Conferences/2023

Internet

https://hdl.handle.net/10356/169509
https://cvpr2023.thecvf.com/Conferences/2023

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