---
license: mit
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - visual-reasoning
  - VQA
  - synthetic
  - domain-robustness
  - CLEVR
pretty_name: Super-CLEVR
size_categories:
  - 100K<n<1M
configs:
  - config_name: questions
    data_files:
      - split: train
        path: viewer/train.parquet
      - split: validation
        path: viewer/validation.parquet
      - split: test
        path: viewer/test.parquet
---

# Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning

**[CVPR 2023 Highlight (top 2.5%)]**

Paper: [Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning](https://arxiv.org/abs/2212.00259)

**Authors:** Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, Alan Yuille

## Dataset Description

Super-CLEVR is a synthetic dataset designed to systematically study the **domain robustness** of visual reasoning models across four key factors:

- **Visual complexity** — varying levels of scene and object complexity
- **Question redundancy** — controlling redundant information in questions
- **Concept distribution** — shifts in the distribution of visual concepts
- **Concept compositionality** — novel compositions of known concepts

## Dataset

Super-CLEVR contains 30k images of vehicles (from [UDA-Part](https://qliu24.github.io/udapart/)) randomly placed in the scenes, with 10 question-answer pairs for each image. The vehicles have part annotations and so the objects in the images can have distinct part attributes.

Here [[link]](https://www.cs.jhu.edu/~zhuowan/zhuowan/SuperCLEVR/obj_part_list/all_objects.html) is the list of objects and parts in Super-CLEVR scenes.

The first 20k images and paired are used for training, the next 5k for validation and the last 5k for testing.

The dataset is available on [Hugging Face](https://huggingface.co/datasets/RyanWW/Super-CLEVR):

| Data                     | Download Link |
|--------------------------|---|
| images                   | [images.zip](https://huggingface.co/datasets/RyanWW/Super-CLEVR/resolve/main/images.zip?download=true) |
| scenes                   | [superCLEVR_scenes.json](https://huggingface.co/datasets/RyanWW/Super-CLEVR/resolve/main/superCLEVR_scenes.json?download=true) |
| questions                | [superCLEVR_questions_30k.json](https://huggingface.co/datasets/RyanWW/Super-CLEVR/resolve/main/superCLEVR_questions_30k.json?download=true) |
| questions (- redundancy) | [superCLEVR_questions_30k_NoRedundant.json](https://huggingface.co/datasets/RyanWW/Super-CLEVR/resolve/main/superCLEVR_questions_30k_NoRedundant.json?download=true) |
| questions (+ redundancy) | [superCLEVR_questions_30k_AllRedundant.json](https://huggingface.co/datasets/RyanWW/Super-CLEVR/resolve/main/superCLEVR_questions_30k_AllRedundant.json?download=true) |

## Dataset Viewer

The interactive **Dataset Viewer** is served from flattened Parquet files under `viewer/` (`train.parquet`, `validation.parquet`, `test.parquet`), exposed as the `questions` config with three splits (200k / 50k / 50k questions, derived from image index per the train/val/test partition above). These contain the question-level fields (`question`, `answer`, `image_filename`, `image_index`, `template_filename`, `question_family_index`, `question_index`); the full functional `program` annotations and scene graphs remain in the canonical `superCLEVR_questions_30k*.json` and `superCLEVR_scenes.json` files.

```python
from datasets import load_dataset

ds = load_dataset("RyanWW/Super-CLEVR", "questions", split="validation")
print(ds[0]["question"], "->", ds[0]["answer"])
```

## Usage

```python
from huggingface_hub import hf_hub_download

# Download a specific file
path = hf_hub_download(
    repo_id="RyanWW/Super-CLEVR",
    filename="superCLEVR_questions_30k.json",
    repo_type="dataset",
)
```

## Citation

```bibtex
@inproceedings{li2023super,
  title={Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning},
  author={Li, Zhuowan and Wang, Xingrui and Stengel-Eskin, Elias and Kortylewski, Adam and Ma, Wufei and Van Durme, Benjamin and Yuille, Alan L},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14963--14973},
  year={2023}
}
```

## Links

- **Code:** [github.com/Lizw14/Super-CLEVR](https://github.com/Lizw14/Super-CLEVR)
- **Paper:** [arxiv.org/abs/2212.00259](https://arxiv.org/abs/2212.00259)

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).