---
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
tags:
- vision-language-models
- visual-illusions
- benchmark
- perception
- probing
size_categories:
- 1K<n<10K
pretty_name: VI-Probe
configs:
- config_name: default
  data_files:
  - split: test
    path: test.parquet
---

# VI-Probe

This dataset accompanies the paper **Do VLMs Perceive or Recall? Probing Visual Perception vs. Memory with Classic Visual Illusions**.

VI-Probe is a controllable visual-illusion benchmark for probing whether vision-language models respond based on visual perception or recall/memorized priors. The dataset includes classic visual illusion cases with original images, graded perturbations, matched controls, visual guide variants, and reverse-prompt variants.

This folder is organized by illusion case for easy inspection, with flat metadata for Hugging Face preview and grouped reporting.

## Files

- `test.parquet`: viewer-ready table with an `image` column and one row per image-prompt pair, including reverse-prompt rows (`is_rp=1`).
- `metadata.csv`: CSV copy of the same metadata for lightweight scripting.
- `summary_by_type.csv`: counts grouped by category, type, reverse-prompt flag, and answer.
- `summary_by_case_type.csv`: counts grouped by class and type.
- `dataset_order.csv`: 27-class order used in the benchmark.
- `class_image_type_counts.csv`: image counts for each class and sample type.

## Folder Structure

The image files are organized by illusion category and case for manual inspection:

```text
VI-Probe/
  README.md
  test.parquet
  metadata.csv
  dataset_order.csv
  summary_by_type.csv
  summary_by_case_type.csv
  class_image_type_counts.csv
  1_illusion_size/
    1_MullerLyerIllusion/
      original/
      perturbed/
      original_control/
      perturbed_control/
      original_with_guide/
      perturbed_with_guide/
    ...
  2_illusion_color/
    12_CornswweetIllusion/
      original/
      perturbed/
      original_control/
      perturbed_control/
      original_with_guide/
      perturbed_with_guide/
    ...
  3_illusion_orientation/
    19_HeringIllusion/
      original/
      perturbed/
      original_control/
      perturbed_control/
      original_with_guide/
      perturbed_with_guide/
    ...
```

Each row in `test.parquet` and `metadata.csv` points to one image-prompt pair. The same image appears twice when both the regular prompt and reverse prompt are included; use `is_rp` to distinguish them.

## Loading

Load the benchmark split directly with Hugging Face Datasets:

```python
from datasets import load_dataset

ds = load_dataset("xxsun/VI-Probe", split="test")

example = ds[0]
image = example["image"]
prompt = example["prompt"]
answer = example["answer"]
```

Useful columns:

| column | description |
|---|---|
| `image` | PIL image object loaded by Hugging Face Datasets |
| `image_path` | relative path to the image file in the repository |
| `case_id` | 1-27 illusion case id |
| `illusion_name` | illusion case name |
| `category` | `size`, `color`, or `orientation` |
| `type` | `original`, `perturbed`, `original_control`, `perturbed_control`, `original_with_guide`, or `perturbed_with_guide` |
| `base_scale` | base stimulus scale |
| `strength` | perturbation strength |
| `is_rp` | whether the row uses the reverse prompt |
| `prompt` | VQA prompt |
| `answer` | `Yes` or `No` |
| `answer_value` | numeric answer, `1` for Yes and `0` for No |

Example grouping for reporting:

```python
df = ds.to_pandas()
summary = (
    df.groupby(["type", "is_rp", "category"])["answer"]
    .value_counts()
    .reset_index(name="count")
)
```

## Overall Counts

- Unique images: 9570
- Metadata rows including reverse prompts: 19140
- Split: test
- Non-reverse prompt rows: 9570
- Reverse prompt rows: 9570

## Non-RP Counts By Type

| type | count |
|---|---:|
| original | 290 |
| perturbed | 2900 |
| original_control | 290 |
| perturbed_control | 2900 |
| original_with_guide | 290 |
| perturbed_with_guide | 2900 |

## Non-RP Counts By Category

| category | count |
|---|---:|
| size | 3993 |
| color | 2310 |
| orientation | 3267 |

## Reporting Recommendation

Use the flat `metadata.csv` for evaluation and group results by `type`, `is_rp`, `category`, and `case_id`. Keep the image files organized by case so per-illusion inspection remains easy.

## Citation

If you use VI-Probe, please cite:

```bibtex
@inproceedings{sun2026vlms,
  title={Do VLMs Perceive or Recall? Probing Visual Perception vs. Memory with Classic Visual Illusions},
  author={Sun, Xiaoxiao and Li, Mingyang and Yuan, Kun and Sun, Min Woo and Endo, Mark and Wu, Shengguang and Li, Changlin and Zhang, Yuhui and Wang, Zeyu and Yeung-Levy, Serena},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={25861--25870},
  year={2026}
}
```