{"url":"/dataset/ubnormal","name":"UBnormal","full_name":"University of Bucharest Abnormal Videos","description_markdown":"UBnormal is a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, the data set introduces abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, the data set includes disjoint sets of anomaly types in the training and test collections of videos.","description_withheld":null,"homepage":"https://github.com/lilygeorgescu/UBnormal/","introduced_date":"2021-11-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/ubnormal-new-benchmark-for-supervised-open","title":"UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection","first_author":"Andra Acsintoae","url":null},"license":{"name":"Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license","url":"https://creativecommons.org/licenses/by-nc-nd/4.0/"},"modalities":[],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Video Anomaly Detection","url":"/task/video-anomaly-detection","datasets_with_task":"/datasets/task/video-anomaly-detection"},{"name":"Weakly-supervised Video Anomaly Detection","url":"/task/weakly-supervised-video-anomaly-detection","datasets_with_task":"/datasets/task/weakly-supervised-video-anomaly-detection"}],"languages":[],"variants":["UBnormal","HR-UBnormal"],"data_loaders":[],"num_papers_in_archive":47,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-ubnormal","task":"Anomaly Detection","dataset_variant":"UBnormal","rows":14,"metrics":["AUC","RBDC","TBDC"],"first_row_in_archive_order":{"model":"STG-NF - Supervised","paper":"/paper/normalizing-flows-for-human-pose-anomaly","metrics":{"AUC":"79.2%"},"code_links":[{"title":"orhir/stg-nf","url":"https://github.com/orhir/stg-nf"}]},"note":"rows are the archive's own order at snapshot; 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not a correctness claim."}},{"paper":"/paper/mulde-multiscale-log-density-estimation-via","title":"MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection","date":"2024-03-21","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/interleaving-one-class-and-weakly-supervised","title":"Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection","date":"2024-01-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/open-vocabulary-video-anomaly-detection","title":"Open-Vocabulary Video Anomaly Detection","date":"2023-11-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/holistic-representation-learning-for","title":"Holistic Representation Learning for Multitask Trajectory Anomaly Detection","date":"2023-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vadclip-adapting-vision-language-models-for","title":"VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection","date":"2023-08-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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