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Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

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 training-free video editing answers to a user prompt rather than to a detector. This paper proposes AVR (Anomaly-aware Video Restoration), which closes that gap with frozen pretrained models alone and ge

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#22 most recent of 237 cs.CV papers we have recorded · ↑ newer: NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian · ↓ older: DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixe
Cite this page: Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration: the #22 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/copy-what-is-seen-generate-what-is-not-training-free-anomaly-aware-video-restora.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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