README.md

metadata

license: cc-by-4.0
dataset_info:
  features:
    - name: COCO
      dtype: image
    - name: T_original
      dtype: string
    - name: T_generated
      dtype: string
    - name: T_negative
      dtype: string
    - name: NeIn
      dtype: image
  splits:
    - name: train
      num_bytes: 71681524914.3
      num_examples: 342775
    - name: validation
      num_bytes: 4945769234.918
      num_examples: 24182
  download_size: 36634351590
  dataset_size: 76627294149.218
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
task_categories:
  - text-to-image
  - image-to-image
language:
  - en
tags:
  - image_editing
  - negation
  - vlm
pretty_name: NeIn
size_categories:
  - 100K<n<1M

NeIn: Telling What You Don’t Want (CVPRW 2025)

Dataset Description

• Homepage: https://tanbuinhat.github.io/NeIn/
• Point of Contact: Nhat-Tan Bui

Dataset Summary

NeIn is the first large-scale for studying negation in text-guided image editing. It comprises 366,957 quintuplets, i.e., source image, original caption, selected object, negative sentence, and target image in total, including 342,775 queries for training and 24,182 queries for benchmarking image editing methods.

Dataset Structure

• COCO (img): The source image from MS-COCO.
• T_original (str): The original caption from COCO of that image.
• T_generated (str): The addition instruction (e.g., "Add a couch.") for generate NeIn's sample and extract selected object.
• T_negative (str): The negative instruction.
• NeIn (img): The target image for negative instruction.

In the context of image editing, given "NeIn" sample and "T_negative" clause, the ground truth corresponds to the “COCO” image. Please refer to our paper for more details.

Citation

If you find this dataset useful, please consider citing our paper:

@article{bui2024nein,
  author={Bui, Nhat-Tan and Hoang, Dinh-Hieu and Trinh, Quoc-Huy and Tran, Minh-Triet and Nguyen, Truong and Gauch, Susan},
  title={NeIn: Telling What You Don't Want},
  journal={arXiv preprint arXiv:2409.06481},
  year={2024}
}