Datasets:
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license: cc-by-4.0
task_categories:
- text-to-image
- text-generation
language:
- en
tags:
- compositionality
- semantic-variation
- text-to-image
- benchmark
- vision-language
pretty_name: SemVarBench
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: train.parquet
- split: test
path: test.parquet
---
# SemVarBench
SemVarBench is the benchmark from the ICLR 2025 paper
[**Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective**](https://openreview.net/forum?id=NWb128pSCb),
designed together with the **SemVarEffect** metric to evaluate the causality between
semantic variations in the input text and the generated image in text-to-image (T2I)
synthesis.
Each example is built around a base caption `T0` and a minimally-edited variant `T1`
that changes the composition (e.g. swapped subject/object or swapped attributes) while
reusing the same words, plus `T2`, a paraphrase of `T1` (passive voice / reordering)
that is semantically equivalent to `T1`. Semantic variations are achieved through two
types of linguistic permutations while avoiding easily predictable literal variations.
This dataset is the flattened version of the `benchmark/` directory in the
[SemVarBench GitHub repository](https://github.com/zhuxiangru/SemVarBench), merging:
- [`trainingset/training_data.txt`](https://github.com/zhuxiangru/SemVarBench/blob/main/benchmark/trainingset/training_data.txt) — the **train** split (10,770 rows).
- [`testset/test_data.txt`](https://github.com/zhuxiangru/SemVarBench/blob/main/benchmark/testset/test_data.txt) — the **test** split (684 rows).
- [`testset_divided_category/`](https://github.com/zhuxiangru/SemVarBench/tree/main/benchmark/testset_divided_category) — 20 per-category slices of the test set, used here to populate the `categories` field.
## Data fields
| Column | Description |
|--------|-------------|
| `id` | Unique example identifier (e.g. `0_61_326`). |
| `T0` | Base caption. |
| `T1` | Semantically varied caption (minimal compositional edit of `T0`). |
| `T2` | Paraphrase of `T1` (passive / reordered), semantically equivalent to `T1`. |
| `categories` | List of contrast-category tags. Populated for the **test** split; empty (`[]`) for the **train** split. A test example may carry more than one tag (118 of 684 do). |
### The 20 test categories
`absolute_location`, `action`, `age`, `appearance`, `color`, `counting`, `direction`,
`height`, `interaction`, `manner`, `material`, `relative_location`, `sentiment`,
`shape`, `size`, `spatio_temporal`, `temperature`, `texture`, `vague_amount`, `weight`.
## Splits
| Split | Rows |
|-------|------|
| `train` | 10,770 |
| `test` | 684 |
Train and test ids are disjoint. The 20 category files together cover exactly the 684
test ids (no more, no fewer).
## Usage
```python
from datasets import load_dataset
ds = load_dataset("zhuxiangru/SemVarBench")
print(ds["test"][0]["T0"], "||", ds["test"][0]["T1"], "||", ds["test"][0]["T2"])
# filter the test set to a single category
color = ds["test"].filter(lambda r: "color" in r["categories"])
print(len(color), "color examples")
```
## Related work
The predecessor dataset [Winoground-T2I](https://github.com/zhuxiangru/Winoground-T2I)
is also available on the Hub at
[`zhuxiangru/Winoground-T2I`](https://huggingface.co/datasets/zhuxiangru/Winoground-T2I).
## Citation
If you find the data in our project useful, please consider citing our work:
```bibtex
@inproceedings{DBLP:conf/iclr/ZhuSSXL00YX25,
author = {Xiangru Zhu and
Penglei Sun and
Yaoxian Song and
Yanghua Xiao and
Zhixu Li and
Chengyu Wang and
Jun Huang and
Bei Yang and
Xiaoxiao Xu},
title = {Evaluating Semantic Variation in Text-to-Image Synthesis: {A} Causal
Perspective},
booktitle = {The Thirteenth International Conference on Learning Representations,
{ICLR} 2025, Singapore, April 24-28, 2025},
publisher = {OpenReview.net},
year = {2025},
url = {https://openreview.net/forum?id=NWb128pSCb},
timestamp = {Thu, 15 May 2025 17:19:05 +0200},
biburl = {https://dblp.org/rec/conf/iclr/ZhuSSXL00YX25.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
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