MaRVL

This is a copy from the original repo: https://github.com/marvl-challenge/marvl-code

If you use this dataset, please cite the original authors:

@inproceedings{liu-etal-2021-visually,
    title = "Visually Grounded Reasoning across Languages and Cultures",
    author = "Liu, Fangyu  and
      Bugliarello, Emanuele  and
      Ponti, Edoardo Maria  and
      Reddy, Siva  and
      Collier, Nigel  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.818",
    pages = "10467--10485",
}

Additional data

In addition to the data available in the original repo, this dataset contains the following columns

• en_translation --> English translation of the hypothesis created using Bing Translate
• left_img --> PIL Image
• right_img--> PIL Image
• resized_left_img --> PIL Image resized
• resized_right_img --> PIL Image resized
• vertically_stacked_img --> PIL image that contains the left and right resized images stacked vertically with a black gutter of 10px
• horizontally_stacked_img --> PIL image that contains the left and right resized images stacked horizontally with a black gutter of 10px

The images were resized using img2dataset:

Show code snippet

Resizer(
  image_size=640,
  resize_mode=ResizeMode.keep_ratio,
  resize_only_if_bigger=True,
)

How to read the images

Due to a bug, the images cannot be stored as PIL.Image.Images directly but need to be converted to dataset.Images-. Hence, to load them, this additional step is required:

from datasets import Image, load_dataset

ds = load_dataset("floschne/marvl", split="sw")
ds.map(
    lambda sample: {
        "left_img_t": [Image().decode_example(img) for img in sample["left_img"]],
        "right_img_t": [Image().decode_example(img) for img in sample["right_img"]],
        "resized_left_img_t": [
            Image().decode_example(img) for img in sample["resized_left_img"]
        ],
        "resized_right_img_t": [
            Image().decode_example(img) for img in sample["resized_right_img"]
        ],
        "vertically_stacked_img_t": [
            Image().decode_example(img) for img in sample["vertically_stacked_img"]
        ],
        "horizontally_stacked_img_t": [
            Image().decode_example(img) for img in sample["horizontally_stacked_img"]
        ],
    },
    remove_columns=[
        "left_img",
        "right_img",
        "resized_left_img",
        "resized_right_img",
        "vertically_stacked_img",
        "horizontally_stacked_img",
    ],
).rename_columns(
    {
        "left_img_t": "left_img",
        "right_img_t": "right_img",
        "resized_left_img_t": "resized_left_img",
        "resized_right_img_t": "resized_right_img",
        "vertically_stacked_img_t": "vertically_stacked_img",
        "horizontally_stacked_img_t": "horizontally_stacked_img",
    }
)