{"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/self-supervised-sparse-representation-for","title":"Self-supervised Sparse Representation for Video Anomaly Detection","arxiv_id":null,"date":"2022-10-23","proceeding":"ECCV 2022 2022 10","authors":["Jhih-Ciang Wu*","He-Yen Hsieh*","Ding-Jie Chen","Chiou-Shann Fuh","Tyng-Luh Liu"],"abstract":"Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence. Existing mainstream VAD techniques are based on either the one-class formulation, which assumes all training data are normal, or weakly-supervised, which requires only video-level normal/anomaly labels. To establish a unified approach to solving the two VAD settings, we introduce a self-supervised sparse representation (S3R) framework that models the concept of anomaly at feature level by exploring the synergy between dictionary-based representation and self-supervised learning. With the learned dictionary, S3R facilitates two coupled modules, en-Normal and de-Normal, to reconstruct snippet-level features and filter out normal-event features. The self-supervised techniques also enable generating samples of pseudo normal/anomaly to train the anomaly detector. We demonstrate with extensive experiments that S3R achieves new state-of-the-art performances on popular benchmark datasets for both one-class and weakly-supervised VAD tasks. Our code is publicly available at https://github.com/louisYen/S3R.","url_abs":"https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730727.pdf","url_pdf":"https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730727.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":"self-supervised-sparse-representation-for","repo_url":"https://github.com/louisYen/S3R","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"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":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"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-1","task":"Anomaly Detection In Surveillance Videos","dataset":"ShanghaiTech Weakly Supervised","model":"S3R","rank_in_archive_order":4,"of":12,"metrics":{"AUC-ROC":"97.48"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on","task":"Anomaly Detection In Surveillance Videos","dataset":"UCF-Crime","model":"S3R","rank_in_archive_order":9,"of":21,"metrics":{"ROC AUC":"85.99"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on-2","task":"Anomaly Detection In Surveillance Videos","dataset":"XD-Violence","model":"S3R (without audio imformation)","rank_in_archive_order":11,"of":17,"metrics":{"AP":"80.26"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-video-anomaly-detection-on","task":"Weakly-supervised Video Anomaly Detection","dataset":"ShanghaiTech Weakly Supervised","model":"S3R","rank_in_archive_order":8,"of":16,"metrics":{"AUC-ROC":"97.48"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}