{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T14:27:41Z","timestamp":1762266461021,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Industrial image anomaly detection is critical for automated manufacturing. However, most existing methods rely on single-category training paradigms, resulting in poor scalability and limited cross-category generalization. These approaches require separate models for each product type and fail to model the complex multi-modal distribution of normal samples in multi-category scenarios. To overcome these limitations, we propose UniCLIP-AD, a unified anomaly detection framework that leverages the general semantic knowledge of CLIP and adapts it to the industrial domain using Low-Rank Adaptation (LoRA). This design enables a single model to effectively handle diverse industrial parts. In addition, we introduce UniAD, a large-scale industrial anomaly detection dataset collected from real production lines. It contains over 25,000 high-resolution images across 7 categories of electronic components, with both pixel-level and image-level annotations. UniAD captures fine-grained, diverse, and realistic defects, making it a strong benchmark for unified anomaly detection. Experiments show that UniCLIP-AD achieves superior performance on UniAD, with an AU-ROC of 92.1% and F1-score of 89.8% in cross-category tasks, outperforming the strongest baselines (CFA and DSR) by 3% AU-ROC and 23.9% F1-score.<\/jats:p>","DOI":"10.3390\/info16110956","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T14:02:09Z","timestamp":1762264929000},"page":"956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UniAD: A Real-World Multi-Category Industrial Anomaly Detection Dataset with a Unified CLIP-Based Framework"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3153-5907","authenticated-orcid":false,"given":"Junyang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2448-6717","authenticated-orcid":false,"given":"Jiuxin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5007-4811","authenticated-orcid":false,"given":"Chengge","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., and Sko\u010daj, D. (2021, January 11\u201317). Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qiang, Y., Cao, J., Zhou, S., Yang, J., Yu, L., and Liu, B. (2025). tGARD: Text-Guided Adversarial Reconstruction for Industrial Anomaly Detection. IEEE Trans. Ind. Inform.","DOI":"10.1109\/TII.2025.3598429"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., and Gehler, P. (2022, January 21\u201324). Towards total recall in industrial anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"78446","DOI":"10.1109\/ACCESS.2022.3193699","article-title":"Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization","volume":"10","author":"Lee","year":"2022","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C. (2019, January 15\u201320). MVTec AD\u2014A comprehensive real-world dataset for unsupervised anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zou, Y., Jeong, J., Pemula, L., Zhang, D., and Dabeer, O. (2022, January 23\u201327). Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20056-4_23"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhu, W., Gao, B.B., Gan, Z., Zhang, J., Gu, Z., Qian, S., Chen, M., and Ma, L. (2024, January 17\u201321). Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02159"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zuo, Z., Wu, Z., Chen, B., and Zhong, X. (2024, January 14\u201319). A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation. Proceedings of the ICASSP 2024\u20142024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Republic of Korea.","DOI":"10.1109\/ICASSP48485.2024.10446766"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wyatt, J., Leach, A., Schmon, S.M., and Willcocks, C.G. (2022, January 21\u201324). Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Z., Zeng, D., Wu, Z., and Jiang, Y.G. (2025). DiffusionAD: Norm-guided one-step denoising diffusion for anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2025.3570494"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hyun, J., Kim, S., Jeon, G., Kim, S.H., Bae, K., and Kang, B.J. (2024, January 1\u20136). Reconpatch: Contrastive patch representation learning for industrial anomaly detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV57701.2024.00205"},{"key":"ref_12","unstructured":"Yuan, J., Gao, C., Jie, P., Xia, X., Huang, S., and Liu, W. (2025, August 15). AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification. Available online: http:\/\/arxiv.org\/abs\/2503.12910."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ma, W., Zhang, X., Yao, Q., Tang, F., Wu, C., Li, Y., Yan, R., Jiang, Z., and Zhou, S.K. (2025, January 11\u201315). Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.00447"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.1109\/TNSE.2022.3163144","article-title":"Tsmae: A novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder","volume":"10","author":"Gao","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5518118","DOI":"10.1109\/TGRS.2024.3399313","article-title":"Memory-augmented autoencoder with adaptive reconstruction and sample attribution mining for hyperspectral anomaly detection","volume":"62","author":"Huo","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106312","DOI":"10.1016\/j.engappai.2023.106312","article-title":"A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder","volume":"123","author":"Li","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Peng, J., Zhang, J., Yi, R., Wang, Y., and Wang, C. (2023, January 17\u201324). Multimodal industrial anomaly detection via hybrid fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00776"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yu, Z., Dong, Z., Yu, C., Yang, K., Fan, Z., and Chen, C.P. (2025). A review on multi-view learning. Front. Comput. Sci., 19.","DOI":"10.1007\/s11704-024-40004-w"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mohammadi, M., Berahmand, K., Sadiq, S., and Khosravi, H. (2025, January 22\u201326). Knowledge tracing with a temporal hypergraph memory network. Proceedings of the International Conference on Artificial Intelligence in Education, Palermo, Italy.","DOI":"10.1007\/978-3-031-99267-4_10"},{"key":"ref_20","unstructured":"Yang, E., Xing, P., Sun, H., Guo, W., Ma, Y., Li, Z., and Zeng, D. (March, January 25). 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jezek, S., Jonak, M., Burget, R., Dvorak, P., and Skotak, M. (2021, January 25\u201327). Deep learning-based defect detection of metal parts: Evaluating current methods in complex conditions. Proceedings of the 2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Brno, Czech Republic.","DOI":"10.1109\/ICUMT54235.2021.9631567"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Cao, Y., Chen, R., and Shen, W. (September, January 28). Rad: A comprehensive dataset for benchmarking the robustness of image anomaly detection. Proceedings of the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), Bari, Italy.","DOI":"10.1109\/CASE59546.2024.10711763"},{"key":"ref_23","unstructured":"Cao, Y., Cheng, Y., Xu, X., Zhang, Y., Sun, Y., Tan, Y., Zhang, Y., Huang, X., and Shen, W. (2025). Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, W., Xu, X., Gu, Y., Zheng, B., Gao, S., and Wu, Y. (2024, January 17\u201321). Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02096"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, W., Zheng, B., Xu, X., Gan, J., Lu, F., Li, X., Ni, N., Tian, Z., Huang, X., and Gao, S. (2025, January 11\u201315). Multi-sensor object anomaly detection: Unifying appearance, geometry, and internal properties. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.00933"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, C.L., Sohn, K., Yoon, J., and Pfister, T. (2021, January 19\u201325). Cutpaste: Self-supervised learning for anomaly detection and localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, Y., Xu, Y., and Wang, Z. (2023, January 17\u201324). Simplenet: A simple network for image anomaly detection and localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"ref_28","unstructured":"Zavrtanik, V., Kristan, M., and Sko\u010daj, D. (October, January 29). Dsr\u2014A dual subspace re-projection network for surface anomaly detection. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_29","unstructured":"Zhou, Q., Pang, G., Tian, Y., He, S., and Chen, J. (2023). Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/956\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T14:22:53Z","timestamp":1762266173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,4]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["info16110956"],"URL":"https:\/\/doi.org\/10.3390\/info16110956","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,4]]}}}