{"url":"/method/adrm-1","slug":"adrm-1","name":"ADRM","full_name":"Adaptive Dynamic Recursive Mapping","full_name_withheld":false,"description_markdown":"**Adaptive Dynamic Recursive Mapping (ADRM)**  \r\n\r\n$$\r\ns_{t+1}=\\psi_{\\alpha}(s_t)=s_t+\\alpha\\,(s_t-s_t^{2}),\\quad \\alpha\\in[-1,1].\r\n$$  \r\n\r\n- $\\alpha>0$ amplifies evidence for abnormality  \r\n- $\\alpha<0$ suppresses false positives  \r\n- $\\alpha=0$ leaves the score unchanged  \r\n\r\nHere, $s_t$ is the anomaly score at step $t$, and the adaptive decision parameter $\\alpha$ is learned jointly with the backbone detector. By recursively mapping the score trajectory, ADRM stabilises detector outputs, magnifies truly anomalous segments, and damps noisy spikes, yielding more reliable video-level anomaly detection under weak supervision and heterogeneous federated settings.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping","paper":"/paper/dual-detector-re-optimization-for-federated","first_author":"Yong Su","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/dual-detector-re-optimization-for-federated"},"source":{"url":"https://ieeexplore.ieee.org/document/11036561","title":"Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/rekkles2/Fed_WSVAD","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Output Functions","url":"/methods/category/output-functions","pwc_aliases":[]},{"area":"Sequential","area_id":"sequential","collection":"Time Series Modules","url":"/methods/category/time-series-modules","pwc_aliases":[]},{"area":"","area_id":"","collection":"Weakly supervised learning","url":"/methods/category/weakly-supervised-learning","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Robustness Methods","url":"/methods/category/robustness-methods","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/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,"n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/anomaly-detection","name":"Anomaly Detection","papers":1},{"task":"/task/anomaly-detection-in-surveillance-videos","name":"Anomaly Detection In Surveillance Videos","papers":1},{"task":"/task/edge-computing","name":"Edge-computing","papers":1},{"task":"/task/federated-learning","name":"Federated Learning","papers":1},{"task":"/task/multiple-instance-learning","name":"Multiple Instance Learning","papers":1},{"task":"/task/personalized-federated-learning","name":"Personalized Federated Learning","papers":1},{"task":"/task/video-anomaly-detection","name":"Video Anomaly Detection","papers":1},{"task":"/task/weakly-supervised-learning","name":"Weakly-supervised Learning","papers":1},{"task":"/task/weakly-supervised-video-anomaly-detection","name":"Weakly-supervised Video Anomaly Detection","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2025","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/adrm-1"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}