Update README.md
Browse files
README.md
CHANGED
|
@@ -13,6 +13,8 @@ datasets:
|
|
| 13 |
language:
|
| 14 |
- en
|
| 15 |
---
|
|
|
|
|
|
|
| 16 |
This is the text parsing and question generation model for the ICCV 2023 paper [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://arxiv.org/abs/2303.11897)
|
| 17 |
|
| 18 |
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image.
|
|
|
|
| 13 |
language:
|
| 14 |
- en
|
| 15 |
---
|
| 16 |
+
Project page: <https://tifa-benchmark.github.io/>
|
| 17 |
+
|
| 18 |
This is the text parsing and question generation model for the ICCV 2023 paper [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://arxiv.org/abs/2303.11897)
|
| 19 |
|
| 20 |
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image.
|