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| A diffusion-based framework for multi-class anomaly detection H He, J Zhang, H Chen, X Chen, Z Li, X Chen, Y Wang, C Wang, L Xie Proceedings of the AAAI conference on artificial intelligence 38 (8), 8472-8480, 2024 | 63 | 2024 |
| Anomalydiffusion: Few-shot anomaly image generation with diffusion model T Hu, J Zhang, R Yi, Y Du, X Chen, L Liu, Y Wang, C Wang Proceedings of the AAAI conference on artificial intelligence 38 (8), 8526-8534, 2024 | 50 | 2024 |
| Remembering normality: Memory-guided knowledge distillation for unsupervised anomaly detection Z Gu, L Liu, X Chen, R Yi, J Zhang, Y Wang, C Wang, A Shu, G Jiang, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 37 | 2023 |
| Face-Adapter for Pre-trained Diffusion Models with Fine-Grained ID and Attribute Control Y Han, J Zhu, K He, X Chen, Y Ge, W Li, X Li, J Zhang, C Wang, Y Liu European Conference on Computer Vision, 20-36, 2024 | 14 | 2024 |
| Phasic content fusing diffusion model with directional distribution consistency for few-shot model adaption T Hu, J Zhang, L Liu, R Yi, S Kou, H Zhu, X Chen, Y Wang, C Wang, L Ma Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 12 | 2023 |
| Rethinking reverse distillation for multi-modal anomaly detection Z Gu, J Zhang, L Liu, X Chen, J Peng, Z Gan, G Jiang, A Shu, Y Wang, ... Proceedings of the AAAI Conference on Artificial Intelligence 38 (8), 8445-8453, 2024 | 10 | 2024 |
