Update README.md

README.md

@@ -31,14 +31,16 @@ First, we pretrain **CXR-BERT-general** from a randomly initialized BERT model v
 
 ## Citation
 
-```
+The corresponding manuscript is accepted to be presented at the [**European Conference on Computer Vision (ECCV) 2022**](https://eccv2022.ecva.net/)
+
+```bibtex
 @misc{https://doi.org/10.48550/arxiv.2204.09817,
-  title = {Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing},
+  doi = {10.48550/ARXIV.2204.09817},
+  url = {https://arxiv.org/abs/2204.09817},
   author = {Boecking, Benedikt and Usuyama, Naoto and Bannur, Shruthi and Castro, Daniel C. and Schwaighofer, Anton and Hyland, Stephanie and Wetscherek, Maria and Naumann, Tristan and Nori, Aditya and Alvarez-Valle, Javier and Poon, Hoifung and Oktay, Ozan},
+  title = {Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing},
   publisher = {arXiv},
   year = {2022},
-  url = {https://arxiv.org/abs/2204.09817},
-  doi = {10.48550/ARXIV.2204.09817},
 }
 ```
 
@@ -99,7 +101,7 @@ This model was developed using English corpora, and thus can be considered Engli
 
 ## Further information
 
-Please refer to the corresponding paper, [Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing](https://arxiv.org/abs/2204.09817) for additional details on the model training and evaluation.
+Please refer to the corresponding paper, ["Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing", ECCV'22](https://arxiv.org/abs/2204.09817) for additional details on the model training and evaluation.
 
 For additional inference pipelines with CXR-BERT, please refer to the [HI-ML GitHub](https://github.com/microsoft/hi-ml/blob/main/multimodal/README.md) repository. The associated source files will soon be accessible through this link.