20230224 img2llm (salesforce#214)

* update img2prompt_vqa to img2llm_vqa.

* fix more.

* update readme.

README.md

@@ -31,7 +31,7 @@
   [Paper](https://arxiv.org/abs/2301.12597), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/examples/blip2_instructed_generation.ipynb)
   > A generic and efficient pre-training strategy that easily harvests development of pretrained vision models and large language models (LLMs) for vision-language pretraining. BLIP-2 beats Flamingo on zero-shot VQAv2 (**65.0** vs **56.3**), establishing new state-of-the-art on zero-shot captioning (on NoCaps **121.6** CIDEr score vs previous best **113.2**). In addition, equipped with powerful LLMs (e.g. OPT, FlanT5), BLIP-2 also unlocks the new **zero-shot instructed vision-to-language generation** capabilities for various interesting applications!
   * Jan 2023, LAVIS is now available on [PyPI](https://pypi.org/project/salesforce-lavis/) for installation!
-  * [Model Release] Dec 2022, released implementation of **Img2LLM-VQA** <br>
+  * [Model Release] Dec 2022, released implementation of **Img2LLM-VQA** (**CVPR 2023**, _"From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models"_, by Jiaxian Guo et al) <br>
   [Paper](https://arxiv.org/pdf/2212.10846.pdf), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/img2llm-vqa/img2llm_vqa.ipynb)
   > A plug-and-play module that enables off-the-shelf use of Large Language Models (LLMs) for visual question answering (VQA). Img2LLM-VQA surpasses Flamingo on zero-shot VQA on VQAv2 (61.9 vs 56.3), while in contrast requiring no end-to-end training! 
   * [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>