| | --- |
| | 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 |
| | --- |
| | |
| | # 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) | |
| | |
| | ## 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). |
| |
|
