Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a "seeing, reasoning, then editing" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Jiang, Le, Huang, Yan, Xu, Zhen, Xu, Yong, Wong, Hau-San, Wu, Si
Modern industrial quality control heavily relies on automated anomaly detection. While few-shot anomaly detection addresses the challenge of limited labeled data, real-world inspection faces a vast diversity of anomaly types, sizes, and shapes. We identify the primary cause for the anomaly detection difficulty as the progressive loss of detect cues as they pass through deep feature extraction pipelines. To counteract the defect cue fading, we propose a Defect Cue-Preserved Structural Feature Refinement model, referred to as DCP-SFR. Recognizing that early-stage cues are paramount, we design a conditional anomaly cue amplification module to produce an initial anomaly score map, which is then enhanced to increase the contrast between anomalous and normal regions. The amplified cues is subsequently used for reconstruction-based anomaly localization, by anchoring attention on true anomaly regions to preserve spatial integrity and prevent drift. Further, we incorporate a structure-aware segmentation refinement stage to improve anomaly segmentation in terms of edge alignment, thereby significantly improve boundary accuracy. On the MVTec AD and VisA benchmarks, DCP-SFR achieves state-of-the-art performance, with an image-level AUROC of 97.3% and a pixel-level AUROC of 98.2%, demonstrating strong cross-domain generalization performance.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Atmospheric turbulence causes spatially varying distortion and blur in long-range imaging, making restoration highly challenging in real-world applications. Supervised methods rely on synthetic training data, whose simulated degradation often cannot faithfully reflect real turbulence. Existing unsupervised methods usually estimate degradation parameters for each frame independently, without exploiting the shared correlations among frames from the same scene. We propose TMFS, an optimization-based and physics-grounded method for unsupervised turbulence restoration. TMFS is based on a physically motivated tilt-then-blur degradation model and represents frame degradations through a shared turbulence structure. By decomposing the distortion and blur of each frame into a scene-shared correlation function and per-frame noise maps, TMFS enables cross-frame information sharing and alleviates the ill-posedness of framewise estimation. Experiments on synthetic and real datasets demonstrate the effectiveness of TMFS and its strong generalization to real turbulence.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表