{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/batchnorm-based-weakly-supervised-video","title":"BatchNorm-based Weakly Supervised Video Anomaly Detection","arxiv_id":"2311.15367","date":"2023-11-26","proceeding":null,"authors":["Yixuan Zhou","Yi Qu","Xing Xu","Fumin Shen","Jingkuan Song","HengTao Shen"],"abstract":"In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises from the inherent ambiguity in temporal annotations of abnormal occurrences. Inspired by the statistical insight that temporal features of abnormal events often exhibit outlier characteristics, we propose a novel method, BN-WVAD, which incorporates BatchNorm into WVAD. In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos. The proposed DFM criterion is also discriminative for anomaly recognition and more resilient to label noise, serving as the additional anomaly score to amend the prediction of the anomaly classifier that is susceptible to noisy labels. Moreover, a batch-level selection strategy is devised to filter more abnormal snippets in videos where more abnormal events occur. The proposed BN-WVAD model demonstrates state-of-the-art performance on UCF-Crime with an AUC of 87.24%, and XD-Violence, where AP reaches up to 84.93%. Our code implementation is accessible at https://github.com/cool-xuan/BN-WVAD.","url_abs":"https://arxiv.org/abs/2311.15367v1","url_pdf":"https://arxiv.org/pdf/2311.15367v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"batchnorm-based-weakly-supervised-video","repo_url":"https://github.com/cool-xuan/bn-wvad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"},{"task_slug":"weakly-supervised-video-anomaly-detection","task_name":"Weakly-supervised Video Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on","task":"Anomaly Detection In Surveillance Videos","dataset":"UCF-Crime","model":"BN-WVAD","rank_in_archive_order":4,"of":21,"metrics":{"ROC AUC":"87.24"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-2","task":"Anomaly Detection In Surveillance Videos","dataset":"XD-Violence","model":"BN-WVAD","rank_in_archive_order":6,"of":17,"metrics":{"AP":"84.93"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.15367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15367"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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