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---
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
- negative-prompt
- diffusion
- 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](mailto:tanb@uark.edu)
### 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](https://arxiv.org/abs/2409.06481) 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}
}
``` |