[1] André Araujo. Deep image features for instance-level recognition and matching. In Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents, pages 1-1, 2020.
[2] Jaehyeok Bae, Jae-Han Lee, and Seyun Kim. Image anomaly detection and localization with position and neighborhood information. arXiv preprint arXiv:2211.12634, 2022.
[3] Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab. Deep autoencoding models for unsupervised anomaly segmentation in brain mr images. In *Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes* 2018, Held in *Conjunction with MICCAI* 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pages 161–169. Springer, 2019.
[4] Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger. Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization. International Journal of Computer Vision, 130(4):947-969, 2022.
[5] Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. Mvtec ad-a comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9592-9600, 2019.
[6] Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4183–4192, 2020.
[7] Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Steger. The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization. arXiv preprint arXiv:2112.09045, 2021.
[8] Luca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, and Daniele De Gregorio. The eyecandies dataset for unsupervised multimodal anomaly detection and localization. In Proceedings of the 16th Asian Conference on Computer Vision (ACCV2022, 2022. ACCV.
[9] Bingyi Cao, Andre Araujo, and Jack Sim. Unifying deep local and global features for image search. In European Conference on Computer Vision, pages 726-743. Springer, 2020.
[10] Yunkang Cao, Qian Wan, Weiming Shen, and Liang Gao. Informative knowledge distillation for image anomaly segmentation. Knowledge-Based Systems, 248:108846, 2022.
[11] Yunkang Cao, Xiaohao Xu, and Weiming Shen. Complementary pseudo multimodal feature for point cloud anomaly detection. arXiv preprint arXiv:2303.13194, 2023.
[12] Xiaoxue Chen, Junchen Liu, Hao Zhao, Guyue Zhou, and Ya-Qin Zhang. Nerrf: 3d reconstruction and view synthesis for transparent and specular objects with neural refractive-reflective fields. arXiv preprint arXiv:2309.13039, 2023.
[13] Niv Cohen and Yedid Hoshen. Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357, 2020.
[14] David Dehaene and Pierre Eline. Anomaly localization by modeling perceptual features. arXiv preprint arXiv:2008.05369, 2020.
[15] Hanqiu Deng and Xingyu Li. Anomaly detection via reverse distillation from one-class embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9737-9746, 2022.
[16] Okwudili M Ezeme, Qusay H Mahmoud, and Akramul Azim. Dream: deep recursive attentive model for anomaly detection in kernel events. IEEE Access, 7:18860-18870, 2019.
[17] Max K Ferguson, AK Ronay, Yung-Tsun Tina Lee, and Kincho H Law. Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart and sustainable manufacturing systems, 2, 2018.