Update README.md

README.md

@@ -16,7 +16,8 @@ language:
 - en
 
 ---
-This is the repo for the paper [PromptCap: Prompt-Guided Task-Aware Image Captioning](https://arxiv.org/abs/2211.09699)
+This is the repo for the paper [PromptCap: Prompt-Guided Task-Aware Image Captioning](https://arxiv.org/abs/2211.09699). This paper is accepted to ICCV 2023 as [PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3](https://openaccess.thecvf.com/content/ICCV2023/html/Hu_PromptCap_Prompt-Guided_Image_Captioning_for_VQA_with_GPT-3_ICCV_2023_paper.html).
+
 
 We introduce PromptCap, a captioning model that can be controlled by natural language instruction. The instruction may contain a question that the user is interested in.
 For example, "what is the boy putting on?". PromptCap also supports generic caption, using the question "what does the image describe?"
@@ -43,7 +44,7 @@ Generate a prompt-guided caption by following:
 import torch
 from promptcap import PromptCap
 
-model = PromptCap("vqascore/promptcap-coco-vqa")  # also support OFA checkpoints. e.g. "OFA-Sys/ofa-large"
+model = PromptCap("tifa-benchmark/promptcap-coco-vqa")  # also support OFA checkpoints. e.g. "OFA-Sys/ofa-large"
 
 if torch.cuda.is_available():
   model.cuda()
@@ -87,7 +88,7 @@ import torch
 from promptcap import PromptCap_VQA
 
 # QA model support all UnifiedQA variants. e.g. "allenai/unifiedqa-v2-t5-large-1251000"
-vqa_model = PromptCap_VQA(promptcap_model="vqascore/promptcap-coco-vqa", qa_model="allenai/unifiedqa-t5-base")
+vqa_model = PromptCap_VQA(promptcap_model="tifa-benchmark/promptcap-coco-vqa", qa_model="allenai/unifiedqa-t5-base")
 
 if torch.cuda.is_available():
   vqa_model.cuda()