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Update multiple anomaly detection papers #45

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Update multiple anomaly detection papers #45

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5 changes: 4 additions & 1 deletion README.md
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Expand Up @@ -76,6 +76,7 @@ In image, video data, it is aimed to classify abnormal images or to segment abno
- Few-Shot Scene-Adaptive Anomaly Detection | **[ECCV' 20]**
- Re Learning Memory Guided Normality for Anomaly Detection | **[Arxiv' 20]** | [`[pdf]`](https://arxiv.org/ftp/arxiv/papers/2101/2101.12382.pdf)
- Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning | **[ICCV' 21]** | [`[pdf]`](https://arxiv.org/pdf/2101.10030.pdf) | [`[code]`](https://github.com/tianyu0207/RTFM)
- Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection | **[MICCAI' 22]** | [`[pdf]`](https://arxiv.org/pdf/2203.12121.pdf) | [`[code]`](https://github.com/tianyu0207/weakly-polyp)

## Image-level anomaly detection

Expand Down Expand Up @@ -127,7 +128,7 @@ In image, video data, it is aimed to classify abnormal images or to segment abno
- Attribute Restoration Framework for Anomaly Detection | **[IEEE Transactions on Multimedia 21]** | [`[pdf]`](https://arxiv.org/abs/1911.10676)
- Modeling the distribution of normal data in pre-trained deep features for anomaly detection | **[ICPR' 20]** | [`[pdf]`](https://arxiv.org/abs/2005.14140) | [`[code]`](https://github.com/ORippler/gaussian-ad-mvtec)
- Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection | **[ICPR' 20]** | [`[pdf]`](https://arxiv.org/abs/2003.01665)
- Deep One-Class Classification via Interpolated Gaussian Descriptor | **[arXiv' 21]** | [`[pdf]`](https://arxiv.org/pdf/2101.10043.pdf) | [`[code]`](https://github.com/tianyu0207/IGD)
- Deep One-Class Classification via Interpolated Gaussian Descriptor | **[AAAI' 22]** | [`[pdf]`](https://arxiv.org/pdf/2101.10043.pdf) | [`[code]`](https://github.com/tianyu0207/IGD)
- Multiresolution Knowledge Distillation for Anomaly Detection | **[CVPR' 21]** | [`[pdf]`](https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html) | [`[code]`](https://github.com/rohban-lab/Knowledge_Distillation_AD)
- Elsa: Energy-based learning for semi-supervised anomaly detection | **[BMVC' 21]** | [`[pdf]`](https://arxiv.org/pdf/2103.15296.pdf) | [`[code]`](https://github.com/archon159/elsa)

Expand Down Expand Up @@ -191,6 +192,8 @@ In image, video data, it is aimed to classify abnormal images or to segment abno
- Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation | **[arXiv' 21]** [`[pdf]`](https://arxiv.org/pdf/2105.14737)
- Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images | **[MICCAI' 21]**| [`[pdf]`](https://arxiv.org/pdf/2103.03423.pdf) | [`[code]`](https://github.com/tianyu0207/CCD)
- Multiresolution Knowledge Distillation for Anomaly Detection | **[CVPR' 21]**| [`[pdf]`](https://arxiv.org/abs/2011.11108)
- Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder | **[arXiv' 22]**| [`[pdf]`](https://arxiv.org/pdf/2203.11725.pdf) | [`[code]`](https://github.com/tianyu0207/MemMC-MAE)


## Contact & Feedback
If you have any suggestions about papers, feel free to mail me :)
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