Unbiased multiple instance learning for weakly supervised video anomaly detection

• Main Authors: LYU, Hui, YUE, Zhongqi, SUN, Qianru, LUO, Bin, CUI, Zhen, ZHANG, Hanwang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/8101; https://ink.library.smu.edu.sg/context/sis_research/article/9104/viewcontent/Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.pdf
• Institution: Singapore Management University

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