ADRM

2000년 도입 · 논문 1편에서 사용

Adaptive Dynamic Recursive Mapping (ADRM) $$ s_{t+1}=\psi_{\alpha}(s_t)=s_t+\alpha\,(s_t-s_t^{2}),\quad \alpha\in[-1,1]. $$ - $\alpha>0$ amplifies evidence for abnormality - $\alpha<0$ suppresses false positives - $\alpha=0$ leaves the score unchanged Here, $s_t$ 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.

출처: Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping

소개 논문: Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping