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---
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license: cc-by-4.0
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dataset_info:
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features:
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- name: COCO
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dtype: image
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- name: T_original
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dtype: string
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- name: T_generated
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dtype: string
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- name: T_negative
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dtype: string
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- name: NeIn
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dtype: image
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splits:
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- name: train
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num_bytes: 71681524914.3
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num_examples: 342775
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- name: validation
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num_bytes: 4945769234.918
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num_examples: 24182
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download_size: 36634351590
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dataset_size: 76627294149.218
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: validation
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path: data/validation-*
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---
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license: cc-by-4.0
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dataset_info:
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features:
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- name: COCO
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dtype: image
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- name: T_original
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dtype: string
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- name: T_generated
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dtype: string
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- name: T_negative
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dtype: string
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- name: NeIn
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dtype: image
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splits:
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- name: train
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num_bytes: 71681524914.3
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num_examples: 342775
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- name: validation
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num_bytes: 4945769234.918
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num_examples: 24182
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download_size: 36634351590
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dataset_size: 76627294149.218
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: validation
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path: data/validation-*
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task_categories:
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- text-to-image
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- image-to-image
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language:
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- en
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tags:
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- image_editing
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- negation
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- vlm
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pretty_name: NeIn
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size_categories:
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- 100K<n<1M
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---
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# NeIn: Telling What You Don’t Want (CVPRW 2025)
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### Dataset Description
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- **Homepage:** https://tanbuinhat.github.io/NeIn/
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- **Point of Contact:** [Nhat-Tan Bui](mailto:tanb@uark.edu)
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### Dataset Summary
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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
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benchmarking image editing methods.
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### Dataset Structure
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- `COCO` (img): The source image from MS-COCO.
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- `T_original` (str): The original caption from COCO of that image.
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- `T_generated` (str): The addition instruction (e.g., "Add a couch.") for generate NeIn's sample and extract selected object.
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- `T_negative` (str): The negative instruction.
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- `NeIn` (img): The target image for negative instruction.
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## Citation
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If you find this dataset useful, please consider citing our paper:
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```
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@article{bui2024nein,
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author={Bui, Nhat-Tan and Hoang, Dinh-Hieu and Trinh, Quoc-Huy and Tran, Minh-Triet and Nguyen, Truong and Gauch, Susan},
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title={NeIn: Telling What You Don't Want},
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journal={arXiv preprint arXiv:2409.06481},
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year={2024}
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}
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```
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