Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomaly detectors stop at a score or a label, while tra...
arXiv:2611.06455v1 Announce Type: new Abstract: Analytics on video archives are bottlenecked by decoding. In a dense pipeline that runs a detector on every frame, decoding is a small fraction of the...
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data.
arXiv:2606. 14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both.
PERSIST redefines shot boundary detection as a task of semantic discrimination, requiring a persistent update of a video’s latent temporal state rather than a transient visual change. It employs a FiLM‑conditioned sinusoidal representation network and a structured discriminator that fuses local change, transient impulse, and return‑to‑trend cues into a single interpretable per‑frame signal. The method achieves comparable recall to leading detectors while significantly reducing false positives from flash, text overlay, and archival artifacts, and it is trained solely on real transitions from ClipShots.
MoG-VLM is a motion‑guided vision‑language model designed for efficient video anomaly detection. It uses a lightweight motion signal, Temporal Gradient Magnitude, to perform content‑aware sampling and activates dense multi‑frame VLM reasoning only around frames with high anomaly likelihood via a hierarchical dual‑stream design. The method achieves state‑of‑the‑art AUC‑ROC scores on four benchmarks and delivers a 1.31× inference speedup on XD‑Violence.