{"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/prodisc-vad-an-efficient-system-for-weakly","title":"ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications","arxiv_id":"2505.02179","date":"2025-05-04","proceeding":null,"authors":["Tao Zhu","Qi Yu","Xinru Dong","Shiyu Li","Yue Liu","Jinlong Jiang","Lei Shu"],"abstract":"Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling this via two synergistic components. The Prototype Interaction Layer (PIL) provides controlled normality modeling using a small set of learnable prototypes, establishing a robust baseline without being overwhelmed by dominant normal data. The Pseudo-Instance Discriminative Enhancement (PIDE) loss boosts separability by applying targeted contrastive learning exclusively to the most reliable extreme-scoring instances (highest/lowest scores). ProDisc-VAD achieves strong AUCs (97.98% ShanghaiTech, 87.12% UCF-Crime) using only 0.4M parameters, over 800x fewer than recent ViT-based methods like VadCLIP, demonstrating exceptional efficiency alongside state-of-the-art performance. Code is available at https://github.com/modadundun/ProDisc-VAD.","url_abs":"https://arxiv.org/abs/2505.02179v1","url_pdf":"https://arxiv.org/pdf/2505.02179v1.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":"prodisc-vad-an-efficient-system-for-weakly","repo_url":"https://github.com/modadundun/ProDisc-VAD","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection-in-surveillance-videos","task_name":"Anomaly Detection In Surveillance Videos"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"supervised-anomaly-detection","task_name":"Supervised Anomaly Detection"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"},{"task_slug":"weakly-supervised-anomaly-detection","task_name":"Weakly-supervised Anomaly Detection"},{"task_slug":"weakly-supervised-video-anomaly-detection","task_name":"Weakly-supervised Video Anomaly Detection"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"set","method_name":"SET"}],"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":"ProDisc-VAD","rank_in_archive_order":2,"of":12,"metrics":{"AUC-ROC":"97.98"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-in-surveillance-videos-on","task":"Anomaly Detection In Surveillance Videos","dataset":"UCF-Crime","model":"ProDisc-VAD","rank_in_archive_order":6,"of":21,"metrics":{"ROC AUC":"87.12"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}