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---
dataset_info:
features:
- name: img_id
dtype: string
- name: turn_index
dtype: int32
- name: source_img
dtype: image
- name: target_img
dtype: image
- name: difference_caption
dtype: string
splits:
- name: train
num_bytes: 5491618579.636
num_examples: 2699
download_size: 3577187403
dataset_size: 5491618579.636
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-to-text
- text-to-image
- image-to-image
language:
- en
tags:
- image-editing
- computer-vision
- image-manipulation
- sequential-editing
- difference-captioning
size_categories:
- 1K<n<10K
---
# Dataset Card for METS (Multiple Edits and Textual Summaries)
## Dataset Summary
METS (Multiple Edits and Textual Summaries) is a dataset of image editing sequences with human-annotated textual summaries describing the differences between original and edited images. The dataset captures cumulative changes after sequences of manipulations, providing ground truth for image difference captioning tasks. METS contains images that have undergone 5, 10, or 15 sequential edits, with human-written summaries describing all visible differences from the original image.
## Dataset Structure
The dataset contains the following fields:
- **img_id** (str): Unique identifier for the image sequence
- **turn_index** (int): The number of edits applied (5, 10, or 15)
- **source_img** (str): Path to the original unedited image
- **target_img** (str): Path to the edited image after the specified number of manipulations
- **difference_caption** (str): Human-written summary of all differences between source and target images
## Licensing and Attribution
This work is licensed under a Creative Commons Attribution 4.0 International License. Please cite the original paper when using this dataset.
## Citation Information
If you find this dataset useful, please consider citing our paper:
```
@inproceedings{Black2025ImProvShow,
title={ImProvShow: Multimodal Fusion for Image Provenance Summarization},
author={Black Alexander and Shi Jing and Fan Yifei and Collomosse John},
booktitle={British Machine Vision Conference (BMVC)},
year={2025}
}
``` |