VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception
Paper • 2308.01525 • Published
Datasets:
image imagewidth (px) 133 6k | file_name stringlengths 10 56 | category int32 1 8 | soft_label list |
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End of preview. Expand in Data Studio
VisAlign: Dataset for Measuring the Alignment between AI and Humans in Visual Perception
This is the open 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, each labeled with an 11-dimensional soft label reflecting human judgments.
- 📄 Paper: arXiv:2308.01525
- 💻 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) |
soft_label |
float32[11] |
Label distribution over the 10 classes + abstention |
Label schema
The 11 dimensions of soft_label correspond, 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". For categories 1–7 labels are one-hot; for category 8 they are soft labels obtained via large-scale crowdsourcing.
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), with crowdsourced soft labels |
Usage
from datasets import load_dataset
ds = load_dataset("jiyounglee0523/VisAlign", split="test")
print(ds[0]["soft_label"], ds[0]["category"])
To evaluate visual alignment, compare your model's output distribution (with an abstention mechanism) against soft_label using the distance metrics described in the paper (e.g., Hellinger distance).
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},
year={2023}
}
License
CC-BY-4.0
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