Where to Verify, How to Correct: Selective Semantic Verification for Weakly Supervised Video Anomaly Detection

Weakly Supervised Video Anomaly Detection (WSVAD) aims to localize frame-level anomalies using only video-level supervision, yet visually dynamic normal activities can still receive high anomaly scores and cause false alarms. Language-guided verification provides complementary semantic evidence for reassessing such elevated responses. However, applying semantic verification to every snippet is costly for long videos and does not necessarily improve anomaly localization. We propose Semantic-Temporal Reprojection (STR), a training-free framework for selective semantic verification of frozen WSVAD detectors. STR retains dense baseline scoring and applies semantic verification to selected elevated responses. Adaptive temporal anchor mining allocates queries across distinct temporal contexts. Structured semantic verification produces a bounded semantic score, while a video-adaptive perception veto limits corrections at extreme baseline responses. Local Gaussian reprojection constrains the remaining corrections to nearby temporal locations. The resulting one-sided correction operates on candidates exposed by the frozen detector and retains or attenuates existing anomaly scores. Experiments on UCF-Crime, XD-Violence, and MSAD show consistent improvements across multiple frozen WSVAD baselines, achieving 90.80% AUC, 89.08% AP, and 88.40% AUC, respectively. Under matched query budgets, adaptive mining outperforms uniform, random, and score-based Top-K selection. Compared with dense verification, STR uses far fewer semantic queries and achieves higher detection performance with lower verification-stage runtime. Code is available at https://anonymous.4open.science/r/STR.