Update README.md

Author: dxli94

README.md

@@ -43,7 +43,32 @@
   * [Model Release] Oct 2022, released implementation of **PNP-VQA** (**EMNLP Findings 2022**, _"Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training"_, by Anthony T.M.H. et al), <br> 
   [Paper](https://arxiv.org/abs/2210.08773), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/pnp-vqa/pnp_vqa.ipynb))
   >  A modular zero-shot VQA framework that requires no PLMs training, achieving SoTA zero-shot VQA performance. 
-    
+
+## Technical Report and Citing LAVIS
+You can find more details in our [technical report](https://arxiv.org/abs/2209.09019).
+
+**If you're using LAVIS in your research or applications, please cite it using this BibTeX**:
+```bibtex
+@inproceedings{li-etal-2023-lavis,
+    title = "{LAVIS}: A One-stop Library for Language-Vision Intelligence",
+    author = "Li, Dongxu  and
+      Li, Junnan  and
+      Le, Hung  and
+      Wang, Guangsen  and
+      Savarese, Silvio  and
+      Hoi, Steven C.H.",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-demo.3",
+    pages = "31--41",
+    abstract = "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks.",
+}
+```
+
+
 ## Table of Contents
   - [Introduction](#introduction)
   - [Installation](#installation)
@@ -293,31 +318,6 @@ We note that models in LAVIS provide no guarantees on their multimodal abilities
 inappropriate behaviors in the future.
 
 
-## Technical Report and Citing LAVIS
-You can find more details in our [technical report](https://arxiv.org/abs/2209.09019).
-
-If you're using LAVIS in your research or applications, please cite using this BibTeX:
-```bibtex
-@inproceedings{li-etal-2023-lavis,
-    title = "{LAVIS}: A One-stop Library for Language-Vision Intelligence",
-    author = "Li, Dongxu  and
-      Li, Junnan  and
-      Le, Hung  and
-      Wang, Guangsen  and
-      Savarese, Silvio  and
-      Hoi, Steven C.H.",
-    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
-    month = jul,
-    year = "2023",
-    address = "Toronto, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2023.acl-demo.3",
-    pages = "31--41",
-    abstract = "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks.",
-}
-}
-```
-
 ## Contact us
 If you have any questions, comments or suggestions, please do not hesitate to contact us at [email protected].