{"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/iterative-weak-self-supervised-classification","title":"Iterative weak/self-supervised classification framework for abnormal events detection","arxiv_id":null,"date":"2021-01-03","proceeding":null,"authors":["Bruno Degardin","Hugo Proença"],"abstract":"The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the ”UBI-Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0167865521000507","url_pdf":"https://www.di.ubi.pt/~hugomcp/doc/Events_PRL.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":"iterative-weak-self-supervised-classification","repo_url":"https://github.com/DegardinBruno/human_self_learning_anomaly","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"abnormal-event-detection-in-video","task_name":"Abnormal Event Detection In Video"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"self-supervised-anomaly-detection","task_name":"Self-Supervised Anomaly Detection"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"semi-supervised-video-classification","task_name":"Semi-Supervised Video Classification"},{"task_slug":"semi-supervised-anomaly-detection","task_name":"Semi-supervised Anomaly Detection"},{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[{"slug":"ubi-fights","name":"UBI-Fights","full_name":"Abnormal Event Detection Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-anomaly-detection-on-ubi","task":"Semi-supervised Anomaly Detection","dataset":"UBI-Fights","model":"SS-Model + WS-Model + Sultani et al.","rank_in_archive_order":1,"of":7,"metrics":{"AUC":"0.846","Decidability":"1.108","EER":"0.216"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-anomaly-detection-on-ubi","task":"Semi-supervised Anomaly Detection","dataset":"UBI-Fights","model":"SS-Model","rank_in_archive_order":2,"of":7,"metrics":{"AUC":"0.819","Decidability":"0.986","EER":"0.284"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}