[arXiv2025] AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection. Paper is avaliable at https://arxiv.org/abs/2511.22595

[arXiv2025] MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples. Paper is avaliable at https://arxiv.org/abs/2511.10047

[CVPR2025 VAND3.0 Challenge, 2rd Winner of Track 1] RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images. The competition repo is available at https://github.com/xrli-U/RoBiS. Paper is available at https://arxiv.org/pdf/2505.21152.

[CVPR2025] Main Element Binarization (MEBin) approach introduced in AnomalyNCD

[CVPR2025] AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios. Paper is available at https://arxiv.org/abs/2410.14379

[ICCV2025] SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning. Paper is available at https://arxiv.org/abs/2410.14987

[TITS2024] Exploiting Low-level Representations for Ultra-Fast Road Segmentation. LFD achieves the best downstream segmentation results in SeaS. The original repo is available at https://github.com/zhouhuan-hust/LFD-RoadSeg. Paper is available at https://arxiv.org/abs/2402.02430

[ICLR2024] MuSc : Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images. The original repo is available at https://github.com/xrli-U/MuSc.