Datasets:

microsoft
/

Do-You-See-Me

license: cdla-permissive-2.0
pretty_name: Do-You-See-Me Dataset
tags:
  - visual-perception
  - vision-language
  - MLLMs
  - dataset
language:
  - en
task_categories:
  - image-text-to-text
papers:
  - title: >-
      Do You See Me: A Multidimensional Benchmark for Evaluating Visual
      Perception in Multimodal LLMs
    url: https://arxiv.org/pdf/2506.02022

DoYouSeeMe

Overview

The DoYouSeeMe benchmark is a comprehensive evaluation framework designed to assess visual perception capabilities in Machine Learning Language Models (MLLMs). This fully automated test suite dynamically generates both visual stimuli and perception-focused questions (VPQA) with incremental difficulty levels, enabling a graded evaluation of MLLM performance across multiple perceptual dimensions. Our benchmark consists of both 2D and 3D photorealistic evaluations of MLLMs.

Theoretical Foundation

The dataset's structure is grounded in established human psychological frameworks that categorize visual perception into core abilities (Chalfant and Scheffelin, 1969). Drawing inspiration from standardized assessments like the Test of Visual Perception Skills (TVPS) (Gardner, 1988) and Motor-Free Visual Perception Test (MVPT) (Colarusso, 2003), DoYouSeeMe adapts these principles to create a systematic evaluation methodology for machine vision systems.

Perceptual Dimensions

The benchmark focuses on seven key dimensions of visual perception:

Shape Discrimination (2D and 3D): Evaluates the ability to recognize shapes.
Joint Shape-Color Discrimination (2D and 3D): Evaluates the ability to jointly recognize shapes and color.
Visual Form Constancy (2D and 3D): Tests MLLM ability to identify a test shape configuration from similarly placed disctractors.
Letter Disambiguation (2D and 3D): Tests the recognition of letters.
Visual Figure-Ground (2D): Evaluates the ability to distinguish the main object from its background under varying conditions.
Visual Closure (2D): Assesses the ability to complete partially obscured shapes by mentally filling in missing information.
Visual Spatial (2D and 3D): Examines the ability to perceive positions of objects relative to oneself and to other objects.

Note: While human visual perception also includes Visual Memory (the ability to remember sequences of presented images), this dimension is omitted from the benchmark as current MLLMs lack short-term visual memory capabilities beyond textual descriptions.

Technical Implementation

The entire dataset generation framework is implemented in Python and uses SVG representations to create visual stimuli with precisely controlled parameters. This approach allows for:

Dynamic generation of test images with systematic variations
Controlled difficulty progression across perception dimensions
Reproducible evaluation conditions
Fine-grained assessment of model performance

Control Parameters

The code and dataset are open-sourced to facilitate further research and advancement in the field of visual perception for artificial intelligence systems.

Paper: DoYouSeeMe Benchmark on arXiv

Code: DoYouSeeMe Github Repo

Dataset Overview and Structure

This repository contains a synthetic dataset exploring seven distinct dimensions of visual perception and processing. Each dimension examines a specific aspect of how we interpret visual information. The benchmark dataset is released as a zip file named dataset.zip in the main folder.

Dataset Structure

The repository is organized into two directories:

2D_DoYouSeeMe
3D_DoYouSeeMe

Each directory consists of separate dimension wise dataset:

2D_DoYouSeeMe/visual_spatial
2D_DoYouSeeMe/visual_figure_ground
2D_DoYouSeeMe/visual_form_constancy
2D_DoYouSeeMe/shape_disambiguation
2D_DoYouSeeMe/shape_color_discrimination
2D_DoYouSeeMe/letter_disambiguation
2D_DoYouSeeMe/visual_closure

3D_DoYouSeeMe/visual_spatial
3D_DoYouSeeMe/visual_form_constancy
3D_DoYouSeeMe/shape_disambiguation
3D_DoYouSeeMe/shape_color_discrimination
3D_DoYouSeeMe/letter_disambiguation

Data Format

Each dimension directory contains:

Images(<xx>.png): Images with controlled variations
dataset_info.csv: Metadata file containing control parameters and ground truth answers for each image

Dataset Generation and Usage

To generate data, run the Python file corresponding to the visual-perception dimension you are interested in. The general command structure is:

python scripts/<dimensionality>/<dimension-name>.py

Replace <dimensionality> with either 2D or 3D.
Replace <dimension-name> with the actual name of the visual-perception dimension (e.g., visual_spatial, shape_disambiguation).

Example: To generate data for the 2D visual_spatial dimension, you would execute:

python scripts/2D/visual_spatial.py

Each python file has a control towards the end, where sweeps are defined for each control parameter listed in Table 1, these can be changed to increase data. For 1) visual_spatial, 2) shape_disambiguation, and 3) shape_color_discrimination a dataset_dump.csv is created in related directory, this dump file captures all the details for each generated image, we then use a dataset_creator.py file (added in all the three dirs) to generate the actual dataset (dataset_info.csv), where multiple perception questions are formulated per image (refer the dataset_creator.py to change number of questions per image). Each visual-perception dim has a dataset_info.csv containing filename, question, answer, and sweep column.

Results

Samples

Visual Spatial

Tests the ability to perceive and understand spatial relationships between objects. Evaluates orientation discrimination and positional awareness.

Sample Question: Starting from the black circle at position (row 1, column 3), how many triangles are there bottom of it in the same row?

Visual Figure-Ground

Examines the ability to distinguish an object from its background. Challenges perception by varying contrast, noise, and complexity.

Sample Question: The figure consists of a Target image, which is embedded in some background noise. Out of the four given options, your task is to pick the option which has the same figure as the target image. Respond as follows: Option <your answer (choose between 1, 2, 3, or 4)>.

Visual Form Constancy

Assesses recognition of shapes despite changes in size, orientation, or context. Tests invariance in visual perception.

Sample Question: The figure consists of a Target image. Out of the four given options, your task is to pick the option which has the same figure as the target image. Respond as follows: Option <your answer (choose between 1, 2, 3, or 4)>.

Shape Disambiguation

Challenges the ability to identify ambiguous shapes that can be interpreted in multiple ways. Explores perceptual flexibility.

Sample Question: Count the total number of triangles in the image, including each concentric triangle separately. For example, if there is one triangle with 2 inner concentric rings, that counts as 3 triangles. Respond with only a number.

Shape Color Discrimination

Tests the ability to differentiate shapes based on color properties while controlling for other visual features.

Sample Question: Count the number of star's that are red.

Letter Disambiguation

Examines recognition of letters under various transformations and distortions. Evaluates robustness of character recognition.

*Sample Question: The image shows one or more letters formed by a grid of small squares. What letter(s) can you identify in this image? Please respond with only the letter(s) you see.*\

Visual Closure

Tests the ability to recognize incomplete figures by mentally filling in missing information. Evaluates gestalt processing.

Sample Question: The figure consists of a target image which is complete, Out of the four given options (which are partially complete), your task is to pick the option which when completed matches the target image. Respond as follows: Option <your answer (choose between 1, 2, 3, or 4)>.

Citation

If you use this benchmark or dataset in your research, please cite our work as follows:

@misc{kanade2025multidimensionalbenchmarkevaluating,
      title={Do You See Me : A Multidimensional Benchmark for Evaluating Visual Perception in Multimodal LLMs}, 
      author={Aditya Kanade and Tanuja Ganu},
      year={2025},
      eprint={2506.02022},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.02022}, 
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

License 📜

The code in this repository is licensed under the MIT License. The dataset is licensed under the Community Data License Agreement - Permissive - Version 2.0 (CDLA-Permissive-2.0).