% Key papers for Unified multi-class anomaly detection (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2021_cutpaste_16777,
  title = {{CutPaste: Self-Supervised Learning for Anomaly Detection and Localization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2023_simplenet_22208,
  title = {{SimpleNet: A Simple Network for Image Anomaly Detection and Localization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23266},
}

@inproceedings{iclr2019_deep_28988,
  title = {{Deep Anomaly Detection with Outlier Exposure}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/772},
}

@inproceedings{cvpr2021_panda_17555,
  title = {{PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_graph_15615,
  title = {{Graph Embedded Pose Clustering for Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2024_realnet_24596,
  title = {{RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30786},
}

@inproceedings{cvpr2023_unbiased_22442,
  title = {{Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21472},
}

@inproceedings{cvpr2023_exploiting_20941,
  title = {{Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21114},
}

@inproceedings{cvpr2025_dinomaly_25951,
  title = {{Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34672},
}

@inproceedings{neurips2022_a_65429,
  title = {{A Unified Model for Multi-class Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55070},
}

@inproceedings{aaai2023_mean_4133,
  title = {{Mean-Shifted Contrastive Loss for Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25309},
}

@inproceedings{cvpr2024_toward_25026,
  title = {{Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29846},
}

@inproceedings{cvpr2025_exploring_26188,
  title = {{Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32783},
}

@inproceedings{aaai2019_multi_101703,
  title = {{Multi-View Anomaly Detection: Neighborhood in Locality Matters}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4418},
}

@inproceedings{neurips2019_transfer_59115,
  title = {{Transfer Anomaly Detection by Inferring Latent Domain Representations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13440},
}

@inproceedings{neurips2020_understanding_61033,
  title = {{Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17108},
}

@inproceedings{cvpr2025_univad_28009,
  title = {{UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34732},
}

@inproceedings{cvpr2025_uninet_28001,
  title = {{UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33170},
}

@inproceedings{cvpr2024_hyperbolic_23735,
  title = {{Hyperbolic Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31749},
}

@inproceedings{neurips2023_recontrast_67752,
  title = {{ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72002},
}

@inproceedings{aaai2024_generating_6126,
  title = {{Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27910},
}

@inproceedings{aaai2025_cnc_8354,
  title = {{CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32856},
}

@inproceedings{aaai2025_boosting_8280,
  title = {{Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33816},
}

@inproceedings{cvpr2024_long_24027,
  title = {{Long-Tailed Anomaly Detection with Learnable Class Names}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31789},
}

@inproceedings{aaai2026_iad_102643,
  title = {{IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37588},
}

@inproceedings{cvpr2025_modeling_26984,
  title = {{Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34421},
}

@inproceedings{neurips2024_resad_74636,
  title = {{ResAD: A Simple Framework for Class Generalizable Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92956},
}

@inproceedings{aaai2025_filter_9194,
  title = {{Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32243},
}

@inproceedings{aaai2025_unveiling_11189,
  title = {{Unveiling Multi-View Anomaly Detection: Intra-view Decoupling and Inter-view Fusion}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33349},
}

@inproceedings{neurips2024_one_74205,
  title = {{One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93345},
}

@inproceedings{neurips2024_towards_74604,
  title = {{Towards Multi-Domain Learning for Generalizable Video Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92985},
}

@inproceedings{neurips2025_adpretrain_79155,
  title = {{ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=mHfpziOtTW},
}

@inproceedings{aaai2026_commonality_104107,
  title = {{Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37636},
}