| --- |
| 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} |
| } |
| ``` |
|
|
