{"url":"/task/weakly-supervised-video-anomaly-detection","name":"Weakly-supervised Video Anomaly Detection","slug":"weakly-supervised-video-anomaly-detection","description_markdown":"Weakly-supervised Video Anomaly Detection (WS-VAD) refers to identifying unusual or anomalous behaviors within video sequences using models trained primarily on video-level labels, without explicit frame-level annotations. Unlike fully-supervised methods, weakly-supervised approaches significantly reduce annotation costs by leveraging coarse labels (e.g., videos labeled as normal or anomalous). The primary challenge of this task is accurately localizing temporal anomalies and effectively distinguishing subtle anomalous activities from normal background events, relying only on limited supervision signals.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":36,"papers_with_code":21,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/weakly-supervised-video-anomaly-detection-on","slug":"weakly-supervised-video-anomaly-detection-on","dataset":"ShanghaiTech Weakly Supervised","dataset_url":"/dataset/shanghaitech-campus","rows_in_archive":16,"metrics":["AUC-ROC","FAR-Normal"],"first_row_in_archive_order":{"model":"DDRO","paper_title":"Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping","paper_url":"/paper/dual-detector-re-optimization-for-federated","paper_date":"2025-06-13","arxiv_id":null,"code_links":[{"title":"rekkles2/Fed_WSVAD","url":"https://github.com/rekkles2/Fed_WSVAD"}],"syntology":null}},{"leaderboard":"/sota/weakly-supervised-video-anomaly-detection-on-1","slug":"weakly-supervised-video-anomaly-detection-on-1","dataset":"UBnormal","dataset_url":"/dataset/ubnormal","rows_in_archive":11,"metrics":["AUC-ROC"],"first_row_in_archive_order":{"model":"DDRO (SSALA)","paper_title":"Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping","paper_url":"/paper/dual-detector-re-optimization-for-federated","paper_date":"2025-06-13","arxiv_id":null,"code_links":[{"title":"rekkles2/Fed_WSVAD","url":"https://github.com/rekkles2/Fed_WSVAD"}],"syntology":null}}],"datasets":[{"url":"/dataset/shanghaitech-campus","name":"ShanghaiTech Campus","full_name":"","num_papers_in_archive":207},{"url":"/dataset/ubnormal","name":"UBnormal","full_name":"University of Bucharest Abnormal Videos","num_papers_in_archive":47}],"subtasks":[],"parent_tasks":[{"url":"/task/video-anomaly-detection","name":"Video Anomaly Detection"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":21,"of":21,"tagged_in_all":36,"items":[{"url":"/paper/real-world-anomaly-detection-in-surveillance","title":"Real-world Anomaly Detection in Surveillance Videos","date":"2018-01-12","arxiv_id":"1801.04264","repositories_listed":9,"syntology":{"n":6,"n_ran":4,"n_unverified":2,"n_pointer_only":4}},{"url":"/paper/weakly-supervised-video-anomaly-detection","title":"Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning","date":"2021-01-25","arxiv_id":"2101.10030","repositories_listed":3,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":5}},{"url":"/paper/weakly-supervised-video-anomaly-detection-2","title":"Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention","date":"2023-09-28","arxiv_id":"2309.16309","repositories_listed":2,"syntology":null},{"url":"/paper/clip-tsa-clip-assisted-temporal-self","title":"CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection","date":"2022-12-09","arxiv_id":"2212.05136","repositories_listed":2,"syntology":null},{"url":"/paper/dual-detector-re-optimization-for-federated","title":"Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping","date":"2025-06-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/prodisc-vad-an-efficient-system-for-weakly","title":"ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications","date":"2025-05-04","arxiv_id":"2505.02179","repositories_listed":1,"syntology":null},{"url":"/paper/ucf-crime-dvs-a-novel-event-based-dataset-for","title":"UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks","date":"2025-03-17","arxiv_id":"2503.12905","repositories_listed":1,"syntology":null},{"url":"/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","arxiv_id":"2401.13551","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/dynamic-erasing-network-based-on-multi-scale","title":"Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection","date":"2023-12-04","arxiv_id":"2312.01764","repositories_listed":1,"syntology":null},{"url":"/paper/batchnorm-based-weakly-supervised-video","title":"BatchNorm-based Weakly Supervised Video Anomaly Detection","date":"2023-11-26","arxiv_id":"2311.15367","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_unverified":2,"n_pointer_only":5}},{"url":"/paper/vadclip-adapting-vision-language-models-for","title":"VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection","date":"2023-08-22","arxiv_id":"2308.11681","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/learning-prompt-enhanced-context-features-for","title":"Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection","date":"2023-06-26","arxiv_id":"2306.14451","repositories_listed":1,"syntology":{"n":9,"n_ran":0,"n_unverified":9,"n_pointer_only":0}},{"url":"/paper/unbiased-multiple-instance-learning-for","title":"Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection","date":"2023-03-22","arxiv_id":"2303.12369","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/dual-memory-units-with-uncertainty-regulation","title":"Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection","date":"2023-02-10","arxiv_id":"2302.05160","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/mgfn-magnitude-contrastive-glance-and-focus","title":"MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection","date":"2022-11-28","arxiv_id":"2211.15098","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-sparse-representation-for","title":"Self-supervised Sparse Representation for Video Anomaly Detection","date":"2022-10-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/overlooked-video-classification-in-weakly","title":"Overlooked Video Classification in Weakly Supervised Video Anomaly Detection","date":"2022-10-13","arxiv_id":"2210.06688","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-in-surveillance-videos","title":"Anomaly detection in surveillance videos using transformer based attention model","date":"2022-06-03","arxiv_id":"2206.01524","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-nonparametric-submodular-video","title":"Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection","date":"2022-03-24","arxiv_id":"2203.12840","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/weakly-supervised-video-anomaly-detection-via","title":"Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning","date":"2021-04-15","arxiv_id":"2104.07268","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/mist-multiple-instance-self-training","title":"MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection","date":"2021-04-04","arxiv_id":"2104.01633","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}}],"syntology_records":11,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}