Awesome

CapMIT1003 Database

The CapMIT1003 database contains captions and clicks collected for images from the MIT1003 database, for which reference eye scanpath are available. The database is distributed as a single SQLite3 database named capmit1003.db. For convenience, a lightweight Python class to access the database is provided (see "Programmatic Usage").

For more information about the data collection process and dataset statistics, please refer to our preprint https://arxiv.org/abs/2305.12380

Column Descriptions

Name	Type	Description
obs_uid	String	Unique identifier for a labeled image-caption pair.
usr_uid	String	Unique identifier for a single user.
start_time	Timestamp	Date and time (absolute) at which image was shown to user.
caption	String	Caption provided by the user.
img_uid	String	Unique identifier for a single image from MIT1003.
img_path	String	File name of image from MIT1003.
click_id	Integer	Ascending identifier that may be used to order clicks.
x	Integer	Horizontal position of click in image pixel coordinates.
y	Integer	Vertical position of click in image pixel coordinates.
click_time	Timestamp	Date and time (absolute) at which user clicked on image.

Programmatic Usage

The file capmit1003.py provides a CapMIT1003 dataset class to query the SQLite3 database. Its only dependency is pandas, a popular library for handling tabular datasets. The following snippet demonstrates how to iterate over all image-caption pairs and load the image, caption and click path. In addition, it downloads and extracts the MIT1003 stimuli images if they are not already present.

from capmit1003 import CapMIT1003

CapMIT1003.download_images()
with CapMIT1003('capmit1003.db') as db:
    image_captions = db.get_captions()
    for pair in image_captions.itertuples(index=False):
        image = imread(pair.img_path)  # e.g., using Pillow, scikit-image, etc.
        caption = pair.caption
        click_path = db.get_click_path(pair.obs_uid)
        xy_coordinates = click_path[['x', 'y']].values

Note the usage with with; this ensures that the database is properly closed after all queries.

Citation

When using the CapMIT1003 dataset, please cite our preprint.

@article{zanca2023contrastive,
  title     = {Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors},
  author    = {Zanca, Dario and Zugarini, Andrea and Dietz, Simon and Altstidl, Thomas R and Ndjeuha, Mark A Turban and Schwinn, Leo and Eskofier, Bjoern},
  journal   = {arXiv preprint arXiv:2305.12380},
  year      = {2023}
}

Please make sure to also cite the original MIT1003 database.

@InProceedings{Judd_2009,
  author    = {Tilke Judd and Krista Ehinger and Fr{\'e}do Durand and Antonio Torralba},
  title     = {Learning to Predict Where Humans Look},
  booktitle = {IEEE International Conference on Computer Vision (ICCV)},
  year      = {2009}
}