-### Saliency-guided Policies
+#### **Adaptive Policies**
* **SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization**
*A F M Shahab Uddin and Mst. Sirazam Monira and Wheemyung Shin and TaeChoong Chung and Sung-Ho Bae*
@@ -455,6 +480,7 @@ AAAI'2023 [[Paper](https://arxiv.org/abs/2306.16612)]
* **MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer**
*Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu*
ICLR'2023 [[Paper](https://openreview.net/forum?id=dRjWsd3gwsm)]
+[[Code](https://github.com/fistyee/MixPro)]
MixPro Framework
+* **LGCOAMix: Local and Global Context-and-Object-Part-Aware Superpixel-Based Data Augmentation for Deep Visual Recognition**
+*Fadi Dornaika, Danyang Sun*
+TIP'2023 [[Paper](https://ieeexplore.ieee.org/document/10348509)]
+[[Code](https://github.com/DanielaPlusPlus/LGCOAMix)]
+
+ LGCOAMix Framework
+
+
+
+* **Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN**
+*Minsoo Kang, Minkoo Kang, Suhyun Kim*
+AAAI'2024 [[Paper](https://arxiv.org/abs/2401.13193)]
+
+ Catch-Up-Mix Framework
+
+## Mixup for Semi-supervised Learning
+
+* **MixMatch: A Holistic Approach to Semi-Supervised Learning**
+*David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel*
+NIPS'2019 [[Paper](https://arxiv.org/abs/1905.02249)]
+[[Code](https://github.com/google-research/mixmatch)]
+
+ MixMatch Framework
+
+
+
+* **Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy**
+*Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu*
+ArXiv'2019 [[Paper](https://arxiv.org/abs/1911.09307)]
+
+ Pani VAT Framework
+
+
+
+* **ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring**
+*David Berthelot, dberth@google.com, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel*
+ICLR'2020 [[Paper](https://openreview.net/forum?id=HklkeR4KPB)]
+[[Code](https://github.com/google-research/remixmatch)]
+
+ ReMixMatch Framework
+
+
+
+* **DivideMix: Learning with Noisy Labels as Semi-supervised Learning**
+*Junnan Li, Richard Socher, Steven C.H. Hoi*
+ICLR'2020 [[Paper](https://arxiv.org/abs/2002.07394)]
+[[Code](https://github.com/LiJunnan1992/DivideMix)]
+
+ DivideMix Framework
+
+
+## Mixup for Multi-modality
+
+* **MixGen: A New Multi-Modal Data Augmentation**
+*Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li*
+arXiv'2023 [[Paper](https://arxiv.org/abs/2206.08358)]
+[[Code](https://github.com/amazon-research/mix-generation)]
+
+* **VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix**
+*Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo*
+arXiv'2022 [[Paper](https://arxiv.org/abs/2206.08919)]
+
+* **Geodesic Multi-Modal Mixup for Robust Fine-Tuning**
+*Changdae Oh, Junhyuk So, Hoyoon Byun, YongTaek Lim, Minchul Shin, Jong-June Jeon, Kyungwoo Song*
+NeurIPS'2023 [[Paper](https://arxiv.org/abs/2203.03897)]
+[[Code](https://github.com/changdaeoh/multimodal-mixup)]
+
+* **PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis**
+*Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos*
+arXiv'2023 [[Paper](https://arxiv.org/abs/2312.12334)]
+
+ PowMix Framework
+
+
+
## Analysis of Mixup
-* Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak.
- - On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. [[NIPS'2019](https://arxiv.org/abs/1905.11001)] [[code](https://github.com/paganpasta/onmixup)]
+* **On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks**
+*Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak*
+NeurIPS'2019 [[Paper](https://arxiv.org/abs/1905.11001)]
+[[Code](https://github.com/paganpasta/onmixup)]
Framework
-* Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou.
- - How Does Mixup Help With Robustness and Generalization? [[ICLR'2021](https://arxiv.org/abs/2010.04819)]
+
+* **How Does Mixup Help With Robustness and Generalization?**
+*Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou*
+ICLR'2021 [[Paper](https://arxiv.org/abs/2010.04819)]
Framework
-* Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge.
- - Towards Understanding the Data Dependency of Mixup-style Training. [[ICLR'2022](https://openreview.net/pdf?id=ieNJYujcGDO)] [[code](https://github.com/2014mchidamb/Mixup-Data-Dependency)]
+
+* **Towards Understanding the Data Dependency of Mixup-style Training**
+*Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge*
+ICLR'2022 [[Paper](https://openreview.net/pdf?id=ieNJYujcGDO)]
+[[Code](https://github.com/2014mchidamb/Mixup-Data-Dependency)]
Framework
-* Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou.
- - When and How Mixup Improves Calibration. [[ICML'2022](https://arxiv.org/abs/2102.06289)]
+
+* **When and How Mixup Improves Calibration**
+*Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou*
+ICML'2022 [[Paper](https://arxiv.org/abs/2102.06289)]
Framework
-* Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao.
- - Over-Training with Mixup May Hurt Generalization. [[ICLR'2023](https://openreview.net/forum?id=JmkjrlVE-DG)]
+
+* **Over-Training with Mixup May Hurt Generalization**
+*Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao*
+ICLR'2023 [[Paper](https://openreview.net/forum?id=JmkjrlVE-DG)]
Framework
-* Junsoo Oh, Chulhee Yun.
- - Provable Benefit of Mixup for Finding Optimal Decision Boundaries. [[ICML'2023](https://chulheeyun.github.io/publication/oh2023provable/)]
-* Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang.
- - On the Pitfall of Mixup for Uncertainty Calibration. [[CVPR'2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.pdf)]
-* Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga.
- - Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study. [[WACV'2023](https://arxiv.org/abs/2211.03946)] [[code](https://github.com/hchoi71/mix-kd)]
-* Soyoun Won, Sung-Ho Bae, Seong Tae Kim.
- - Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability. [[arXiv'2023](https://arxiv.org/abs/2303.14608)]
+
+* **Provable Benefit of Mixup for Finding Optimal Decision Boundaries**
+*Junsoo Oh, Chulhee Yun*
+ICML'2023 [[Paper](https://chulheeyun.github.io/publication/oh2023provable/)]
+
+* **On the Pitfall of Mixup for Uncertainty Calibration**
+*Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang*
+CVPR'2023 [[Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.pdf)]
+
+* **Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study**
+*Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga*
+WACV'2023 [[Paper](https://arxiv.org/abs/2211.03946)]
+[[Code](https://github.com/hchoi71/mix-kd)]
+
+* **Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability**
+*Soyoun Won, Sung-Ho Bae, Seong Tae Kim*
+arXiv'2023 [[Paper](https://arxiv.org/abs/2303.14608)]
+
+* **Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup**
+*Damien Teney, Jindong Wang, Ehsan Abbasnejad*
+arXiv'2023 [[Paper](https://arxiv.org/abs/2305.16817)]
+
+
## Contribution
diff --git a/docs/en/awesome_selfsup/MIM.md b/docs/en/awesome/awesome_selfsup.md
similarity index 100%
rename from docs/en/awesome_selfsup/MIM.md
rename to docs/en/awesome/awesome_selfsup.md
diff --git a/docs/en/awesome_mixups/Mixup_SSL.md b/docs/en/awesome_mixups/Mixup_SSL.md
deleted file mode 100644
index 6a28a60..0000000
--- a/docs/en/awesome_mixups/Mixup_SSL.md
+++ /dev/null
@@ -1,342 +0,0 @@
-# Awesome Mixup Methods for Self- and Semi-supervised Learning
-
- [](https://awesome.re)  
-
-**We summarize mixup methods proposed for self- and semi-supervised visual representation learning.**
-We are working on a survey of mixup methods. The list is on updating.
-
-* To find related papers and their relationships, check out [Connected Papers](https://www.connectedpapers.com/), which visualizes the academic field in a graph representation.
-* To export BibTeX citations of papers, check out [ArXiv](https://arxiv.org/) or [Semantic Scholar](https://www.semanticscholar.org/) of the paper for professional reference formats.
-
-## Table of Contents
-
- - [Mixup for Self-supervised Learning](#mixup-for-self-supervised-learning)
- - [Mixup for Semi-supervised Learning](#mixup-for-semi-supervised-learning)
- - [Contribution](#contribution)
- - [Related Project](#related-project)
-
-## Mixup for Self-supervised Learning
-
-* **MixCo: Mix-up Contrastive Learning for Visual Representation**
-*Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun*
-NIPSW'2020 [[Paper](https://arxiv.org/abs/2010.06300)]
-[[Code](https://github.com/Lee-Gihun/MixCo-Mixup-Contrast)]
-
- MixCo Framework
-
-
-
-* **Hard Negative Mixing for Contrastive Learning**
-*Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus*
-NIPS'2020 [[Paper](https://arxiv.org/abs/2010.01028)]
-[[Code](https://europe.naverlabs.com/mochi)]
-
- MoCHi Framework
-
-
-## Mixup for Semi-supervised Learning
-
-* **MixMatch: A Holistic Approach to Semi-Supervised Learning**
-*David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel*
-NIPS'2019 [[Paper](https://arxiv.org/abs/1905.02249)]
-[[Code](https://github.com/google-research/mixmatch)]
-
- MixMatch Framework
-
-
-
-* **Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy**
-*Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu*
-ArXiv'2019 [[Paper](https://arxiv.org/abs/1911.09307)]
-
- Pani VAT Framework
-
-
-
-* **ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring**
-*David Berthelot, dberth@google.com, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel*
-ICLR'2020 [[Paper](https://openreview.net/forum?id=HklkeR4KPB)]
-[[Code](https://github.com/google-research/remixmatch)]
-
- ReMixMatch Framework
-
-
-
-* **DivideMix: Learning with Noisy Labels as Semi-supervised Learning**
-*Junnan Li, Richard Socher, Steven C.H. Hoi*
-ICLR'2020 [[Paper](https://arxiv.org/abs/2002.07394)]
-[[Code](https://github.com/LiJunnan1992/DivideMix)]
-
- DivideMix Framework
-
-
-
-* **Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning**
-*Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng*
-NIPS'2021 [[Paper](https://arxiv.org/abs/2102.06605)]
-[[Code](https://github.com/vanint/core-tuning)]
-
- Core-Tuning Framework
-
-
-
-* **MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection**
-*JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak*
-CVPR'2022 [[Paper](https://user-images.githubusercontent.com/44519745/225082975-4143e7f5-8873-433c-ab6f-6caa615f7120.png)]
-[[Code](https://github.com/jongmokkim/mix-unmix)]
-
- MUM Framework
-
-
-
-* **Decoupled Mixup for Data-efficient Learning**
-*Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li*
-NIPS'2023 [[Paper](https://arxiv.org/abs/2203.10761)]
-[[Code](https://github.com/Westlake-AI/openmixup)]
-
- DFixMatch Framework
-
-
-
-* **Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise**
-*Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi*
-Arxiv'2023 [[Paper](https://arxiv.org/abs/2308.06861)]
-[[Code](https://github.com/Fahim-F/ManifoldDivideMix)]
-
- MixEMatch Framework
-
-
-## Contribution
-
-Feel free to send [pull requests](https://github.com/Westlake-AI/openmixup/pulls) to add more links with the following Markdown format. Notice that the Abbreviation, the code link, and the figure link are optional attributes. Current contributors include: Siyuan Li ([@Lupin1998](https://github.com/Lupin1998)) and Zicheng Liu ([@pone7](https://github.com/pone7)).
-
-```markdown
-* **TITLE**
-*AUTHER*
-PUBLISH'YEAR [[Paper](link)] [[Code](link)]
-
- ABBREVIATION Framework
-
-
-```
-
-## Related Project
-
-- [Awesome-Mixup](https://github.com/Westlake-AI/Awesome-Mixup): Awesome List of Mixup Augmentation Papers for Visual Representation Learning.
-- [Awesome-Mix](https://github.com/ChengtaiCao/Awesome-Mix): An awesome list of papers for `A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability, we categorize them based on our proposed taxonomy`.
-- [survery-image-mixing-and-deleting-for-data-augmentation](https://github.com/humza909/survery-image-mixing-and-deleting-for-data-augmentation): An awesome list of papers for `Survey: Image Mixing and Deleting for Data Augmentation`.
-- [awesome-mixup](https://github.com/demoleiwang/awesome-mixup): A collection of awesome papers about mixup.
-- [awesome-mixed-sample-data-augmentation](https://github.com/JasonZhang156/awesome-mixed-sample-data-augmentation): A collection of awesome things about mixed sample data augmentation.
-- [data-augmentation-review](https://github.com/AgaMiko/data-augmentation-review): List of useful data augmentation resources.
