Update README.md

README.md

@@ -7,14 +7,21 @@ library_name: ml-4m
 
 # 4M: Massively Multimodal Masked Modeling
 
-*David Mizrahi\*, Roman Bachmann\*, Oğuzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir*
+*A framework for training any-to-any multimodal foundation models. <br>Scalable. Open-sourced. Across tens of modalities and tasks.*
 
-Official implementation and pre-trained models for "4M: Massively Multimodal Masked Modeling" (NeurIPS 2023).
+[`Website`](https://4m.epfl.ch) | [`GitHub`](https://github.com/apple/ml-4m) | [`BibTeX`](#citation)  
 
-[`Website`](https://4m.epfl.ch) | [`Paper`](https://arxiv.org/abs/2312.06647) | [`GitHub`](https://github.com/apple/ml-4m)
+Official implementation and pre-trained models for :
+
+[**4M: Massively Multimodal Masked Modeling**](https://arxiv.org/abs/2312.06647), NeurIPS 2023 (Spotlight) <br>
+*[David Mizrahi](https://dmizrahi.com/)\*, [Roman Bachmann](https://roman-bachmann.github.io/)\*, [Oğuzhan Fatih Kar](https://ofkar.github.io/), [Teresa Yeo](https://aserety.github.io/), [Mingfei Gao](https://fly6464.github.io/), [Afshin Dehghan](https://www.afshindehghan.com/), [Amir Zamir](https://vilab.epfl.ch/zamir/)*
+
+[**4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities**](https://arxiv.org/abs/2406.09406), arXiv 2024 <br>
+*[Roman Bachmann](https://roman-bachmann.github.io/)\*, [Oğuzhan Fatih Kar](https://ofkar.github.io/)\*, [David Mizrahi](https://dmizrahi.com/)\*, [Ali Garjani](https://garjania.github.io/), [Mingfei Gao](https://fly6464.github.io/), [David Griffiths](https://www.dgriffiths.uk/), [Jiaming Hu](https://scholar.google.com/citations?user=vm3imKsAAAAJ&hl=en), [Afshin Dehghan](https://www.afshindehghan.com/), [Amir Zamir](https://vilab.epfl.ch/zamir/)*
 
 4M is a framework for training "any-to-any" foundation models, using tokenization and masking to scale to many diverse modalities. 
-Models trained using 4M can perform a wide range of vision tasks, transfer well to unseen tasks and modalities, and are flexible and steerable multimodal generative models.
+Models trained using 4M can perform a wide range of vision tasks, transfer well to unseen tasks and modalities, and are flexible and steerable multimodal generative models. 
+We are releasing code and models for "4M: Massively Multimodal Masked Modeling" (here denoted 4M-7), as well as "4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities" (here denoted 4M-21).
 
 
 ## Installation
@@ -35,12 +42,19 @@ Please see https://github.com/apple/ml-4m/blob/main/README_TOKENIZATION.md for m
 
 If you find this repository helpful, please consider citing our work:
 ```
-@inproceedings{mizrahi20234m,
+@inproceedings{4m,
     title={{4M}: Massively Multimodal Masked Modeling},
     author={David Mizrahi and Roman Bachmann and O{\u{g}}uzhan Fatih Kar and Teresa Yeo and Mingfei Gao and Afshin Dehghan and Amir Zamir},
     booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
     year={2023},
 }
+
+@article{4m21,
+    title={{4M-21}: An Any-to-Any Vision Model for Tens of Tasks and Modalities},
+    author={Roman Bachmann and O{\u{g}}uzhan Fatih Kar and David Mizrahi and Ali Garjani and Mingfei Gao and David Griffiths and Jiaming Hu and Afshin Dehghan and Amir Zamir},
+    journal={arXiv 2024},
+    year={2024},
+}
 ```
 
 ## License