The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance. To mitigate visual noise and address privacy concerns, recent work has shifted to pose-based VAD, which focuses on motion dynamics rather than raw video data.
arXiv:2604.11390v3 Announce Type: replace Abstract: While Hyperspectral Anomaly Detection (HAD) excels at identifying sparse targets in complex scenes, existing models remain trapped in a scalar "rec...
arXiv:2608. 05069v1 Announce Type: cross Abstract: Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance.
The paper introduces Uni-DSM, a unified score-driven framework that uses denoising score matching to model video anomaly patterns for both detection and anticipation. It employs a shared noise-conditioned score transformer backbone with scene-dependent embeddings and motion-aware weighting, and differentiates between VAD and VAA through autoregressive denoising and a self-distilled denoising mechanism. Experiments on multiple benchmarks show state‑of‑the‑art performance for both tasks while maintaining high efficiency.
Video anomaly detection (VAD) is a fundamental and safety-critical task in computer vision. Recent generative approaches detect anomalies from a distributional perspective, but remain limited by local...
arXiv:2505.02393v3 Announce Type: replace Abstract: Most existing video anomaly detectors rely on RGB frames alone, which limit their ability to capture abrupt or transient motion cues that are criti...