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---
dataset_info:
features:
- name: implicit_prompt
dtype: string
- name: explicit_prompt
dtype: string
- name: superficial_prompt
dtype: string
- name: explicit_image
sequence: image
- name: superficial_image
sequence: image
- name: scene_scoring
dtype: string
- name: real_scoring
dtype: string
- name: category
dtype: string
- name: law
dtype: string
splits:
- name: test
num_bytes: 568698994
num_examples: 227
download_size: 568517703
dataset_size: 568698994
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: apache-2.0
---
<img src="./teaser.png" align="center">
# Science-T2I-C Benchmark
## Resources
- [Website](https://jialuo-li.github.io/Science-T2I-Web/)
- [arXiv: Paper](https://arxiv.org/abs/2504.13129)
- [GitHub: Code](https://github.com/Jialuo-Li/Science-T2I)
- [Huggingface: SciScore](https://huggingface.co/Jialuo21/SciScore)
- [Huggingface: Science-T2I-Trainset](https://huggingface.co/datasets/Jialuo21/Science-T2I-Trainset)
## Benchmark Collection and Processing
- Science-T2I-C is generated using the identical procedure as the training data, with a key adjustment to the prompts. This test set pushes the model further by introducing more intricate scenarios, incorporating contextual details like specific scene settings and diverse situations. Prompts in Science-T2I-C might include phrases like "in a bedroom" or "on the street," thereby adding spatial and contextual variety. This heightened complexity assesses the model's capacity to adapt to more nuanced and less constrained environments.
- To evaluate the model's understanding of implicit prompts and its ability to connect them with visual content, we employ a comparative image selection task. Specifically, we present the model with an implicit prompt and two distinct images. The model's objective is to analyze the prompt and then choose the image that best aligns with the overall meaning conveyed by that prompt. The specifics of this process are outlined in the EVAL CODE.
## Benchmarking LMM&VLM
Most existing VLMs struggle to select the correct image based on scientific knowledge, with performance often resembling random guessing. Similarly, LMMs face challenges in this area. However, SciScore stands out by demonstrating exceptional performance, achieving human-level accuracy after being trained on Science-T2I.
<img src="./exp.png" align="center">
## Citation
```
@misc{li2025sciencet2iaddressingscientificillusions,
title={Science-T2I: Addressing Scientific Illusions in Image Synthesis},
author={Jialuo Li and Wenhao Chai and Xingyu Fu and Haiyang Xu and Saining Xie},
year={2025},
eprint={2504.13129},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.13129},
}
``` |