2 citations · 2 across the 3 of their papers we have counts for
5 papers
Referring Industrial Anomaly Segmentation
Pengfei Yue, Xiaokang Jiang, Yilin Lu +3
Industrial Anomaly Detection (IAD) is vital for manufacturing, yet traditional methods face significant challenges: unsupervised approaches yield rough localizations requiring manu…
Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection
Yilin Lu, Jianghang Lin, Linhuang Xie +5
Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as l…
Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
Jianghang Lin, Yilin Lu, Yunhang Shen +4
Semi-Supervised Instance Segmentation (SSIS) involves classifying and grouping image pixels into distinct object instances using limited labeled data. This learning paradigm usuall…
AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis
Zhangyu Lai, Yilin Lu, Xinyang Li +5
While existing anomaly synthesis methods have made remarkable progress, achieving both realism and diversity in synthesis remains a major obstacle. To address this, we propose Anom…
AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion
Jie Hu, Yawen Huang, Yilin Lu +4
Anomaly synthesis is one of the effective methods to augment abnormal samples for training. However, current anomaly synthesis methods predominantly rely on texture information as…