README.md

---
license: cc-by-4.0
configs:
- config_name: Isle-Brick-V2
  data_files:
  - split: test
    path: Isle-Brick-V2/*
  features:
  - name: image
    dtype: image
  - name: Q1
    dtype: int64
  - name: Q2
    dtype: int64
  - name: Q3
    dtype: string
  - name: Q4
    sequence: string
  - name: Q5
    sequence: string
  - name: Q6
    dtype: string
  - name: Q7
    sequence: string
- config_name: Isle-Brick-V2-no_object
  data_files:
  - split: test
    path: Isle-Brick-V2-no_object/*
  features:
  - name: image
    dtype: image
  - name: Q5
    sequence: string
- config_name: Isle-Brick-V2-visual_hint
  data_files:
  - split: test
    path: Isle-Brick-V2-visual_hint/*
  features:
  - name: image
    dtype: image
  - name: Q5
    sequence: string
- config_name: Isle-Brick-V2-human
  data_files:
  - split: test
    path: Isle-Brick-V2-human/*
  features:
  - name: image
    dtype: image
  - name: Q5
    sequence: string
- config_name: Isle-Brick-V2-zoom
  data_files:
  - split: test
    path: Isle-Brick-V2-zoom/*
  features:
  - name: image
    dtype: image
  - name: Q5
    sequence: string
  - name: zoom
    dtype: string
- config_name: Isle-Brick-V1
  data_files:
  - split: test
    path: Isle-Brick-V1/*
  features:
  - name: image
    dtype: image
  - name: prompt
    dtype: string
  - name: label
    dtype: int64
- config_name: Isle-Dots
  data_files:
  - split: test
    path: Isle-Dots/*
  features:
  - name: image
    dtype: image
  - name: level
    dtype: int64
  - name: prompt
    dtype: string
  - name: label
    dtype: int64
task_categories:
- visual-question-answering
tags:
- VPT
pretty_name: Isle
size_categories:
- n<1K
---

<p align="center">
<img src="isle.png" alt="" width="200">
</p>

## Dataset Details

The **Isle (I spy with my little eye)** dataset helps researchers study *visual perspective taking* (VPT), scene understanding, and spatial reasoning.

Visual perspective taking is the ability to imagine the world from someone else's viewpoint. This skill is important<br>
for everyday tasks like driving safely, coordinating actions with others, or knowing when it's your _turn to speak_.

This dataset includes high-quality images (over 11 Mpix) and consists of three subsets:

- **Isle-Bricks v1**
- **Isle-Bricks v2**
- **Isle-Dots**

The subsets **Isle-Bricks v1** and **Isle-Dots** come from the study *Seeing Through Their Eyes: Evaluating Visual Perspective Taking in Vision Language Models*,
and were created to test Vision Language Models (VLMs).

**Isle-Bricks v2** comes from the study: *Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models* and provides additional images of Lego minifigures from two viewpoints *(See Figure 1)*:

- **surface-level** view
- **bird’s eye** view

<p align="center">
  <img src="example.png" alt="" width="900">
  <figcaption align="center">
    <h3>
      Figure 1. Example images from the datasets: bottom left, Isle-Brick v1
      bottom right, Isle-Dots; top left, Isle-Dots v2 (surface-level);
      and top right, Isle-Dots v2 (bird’s-eye view).
    </h3>
  </figcaption>
</p>


The Isle-Bricks v2 subset includes seven questions (Q1–Q7) to test visual perspective taking and related skills:

- **Q1:** _List and count all objects in the image that are not humanoid minifigures._
- **Q2:** _How many humanoid minifigures are in the image?_
- **Q3:** _Are the humanoid minifigure and the object on the same surface?_
- **Q4:** _In which cardinal direction (north, west, east, or south) is the object located relative to the humanoid minifigure?_
- **Q5:** _Which direction (north, west, east, or south) is the humanoid minifigure facing?_
- **Q6:** _Assuming the humanoid minifigure can see and its eyes are open, does it see the object?_
- **Q7:** _From the perspective of the humanoid minifigure, where is the object located relative to it (front, left, right, or back)?_

**Psychologists can also use this dataset to study human visual perception and understanding.**

# Another related dataset is [BlenderGaze](https://huggingface.co/datasets/Gracjan/BlenderGaze), containing over **2,000** images generated using Blender.

## Dataset Sources

- **Repository:** [GitHub](https://github.com/GracjanGoral/ISLE)
- **Papers:** [Isle-Brick-V1, Isle-Dots](https://arxiv.org/abs/2409.12969), [Isle-Brick-V2](https://arxiv.org/abs/2505.03821)

## Annotation Process

- Three annotators labeled images, and final answers were based on the majority vote.
- Annotators agreed on labels over 99% of the time.

## Citation

```bibtex
@misc{góral2025recognitionevaluatingvisualperspective,
      title={Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models}, 
      author={Gracjan Góral and Alicja Ziarko and Piotr Miłoś and Michał Nauman and Maciej Wołczyk and Michał Kosiński},
      year={2025},
      eprint={2505.03821},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.03821}, 
}

@misc{góral2024seeingeyesevaluatingvisual,
      title={Seeing Through Their Eyes: Evaluating Visual Perspective Taking in Vision Language Models}, 
      author={Gracjan Góral and Alicja Ziarko and Michal Nauman and Maciej Wołczyk},
      year={2024},
      eprint={2409.12969},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.12969}, 
}
```
```bibtex
Góral, G., Ziarko, A., Miłoś, P., Nauman, M., Wołczyk, M., & Kosiński, M. (2025). Beyond recognition: Evaluating visual perspective taking in vision language models. arXiv. https://arxiv.org/abs/2505.03821

Góral, G., Ziarko, A., Nauman, M., & Wołczyk, M. (2024). Seeing through their eyes: Evaluating visual perspective taking in vision language models. arXiv. https://arxiv.org/abs/2409.12969
```