VisAlign: Dataset for Measuring the Alignment between AI and Humans in Visual Perception

This is the test set of VisAlign (NeurIPS 2023 Datasets and Benchmarks Track), a dataset for measuring the degree of alignment between AI models and humans in visual perception. It contains 900 images across 8 categories.

Ground-truth labels are withheld — to evaluate your model, submit your predictions to the VisAlign Leaderboard.

• 📄 Paper: arXiv:2308.01525
• 🏆 Leaderboard: huggingface.co/spaces/jiyounglee0523/leaderboard
• 💻 Code: github.com/jiyounglee-0523/VisAlign

Dataset Structure

Each sample has:

Column	Type	Description
image	Image	The image
file_name	string	Original file name
category	int32	Test set category, 1–8 (see below)

Categories

Category	Group	#	Description
1	Must-Act	100	Unaltered samples of the 10 classes
2	Must-Act	100	Animals in incongruous backgrounds (generated with Stable Diffusion)
3	Must-Act	100	Category 1 samples with adversarial perturbation (FGSM)
4	Must-Abstain	100	Objects that do not belong to any of the 10 classes
5	Must-Abstain	100	Chimeras combining features of two different animals
6	Must-Abstain	100	Mammals biologically close to the 10 target mammals
7	Must-Abstain	100	Non-photorealistic styles (e.g., drawings, sculptures)
8	Uncertain	200	Images cropped at varying sizes/regions or corrupted with one of 15 corruption types (intensity 1–10)

Prediction format

For leaderboard submission, your model should output an 11-dimensional distribution per image, corresponding, in order, to:

[tiger, zebra, camel, giraffe, elephant, rhino, gorilla, bear, kangaroo, human, abstain]

The last dimension (abstain) represents "none of the 10 mammals / uncertain / unrecognizable".

Usage

from datasets import load_dataset

ds = load_dataset("jiyounglee0523/VisAlign", split="test")
print(ds[0]["file_name"], ds[0]["category"])

Citation

@article{lee2023visalign,
  title={Visalign: Dataset for measuring the alignment between ai and humans in visual perception},
  author={Lee, Jiyoung and Kim, Seungho and Won, Seunghyun and Lee, Joonseok and Ghassemi, Marzyeh and Thorne, James and Choi, Jaeseok and Kwon, O-Kil and Choi, Edward},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={77119--77148},
  year={2023}
}

License

CC-BY-4.0