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20222026
most citedA Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation

5 citations · 9 across the 11 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

Distilling Image Prototypes for Guided Test-Time Adaptation

Liwen Wang, Xingbo Dong, Iman Yi Liao +4

Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from noisy pseudo-labels and catastro…

cs.CV2026

3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement

Deyin Liu, Jicheng Xu, Lin Yuanbo Wu +4

Human image animation, which aims to generate a video of a reference subject following a provided action sequence, has received increasing research interest. With the development o…

cs.CV2026

Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing

Deyin Liu, Yisheng Ding, Zhe Jin +3

Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but fundamentally fail to prese…

cs.CV2025

Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation

Bingrui Zhao, Lin Yuanbo Wu, Xiangtian Fan +5

Referring Video Object Segmentation (RVOS) aims to segment an object of interest throughout a video based on a language description. The prominent challenge lies in aligning static…

cs.CV2025

Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization

Jingqi Wu, Hanxi Li, Lin Yuanbo Wu +3

Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during…

cs.CV2025

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection

Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +4

In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within te…