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--- |
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dataset_info: |
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features: |
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- name: input_test |
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dtype: image |
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- name: input_gt |
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dtype: image |
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- name: exemplar_input |
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dtype: image |
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- name: exemplar_edit |
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dtype: image |
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- name: instruction |
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dtype: string |
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- name: og_description |
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dtype: string |
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- name: edit_description |
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dtype: string |
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- name: input_test_path |
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dtype: string |
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- name: input_gt_path |
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dtype: string |
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- name: exemplar_input_path |
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dtype: string |
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- name: exemplar_edit_path |
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dtype: string |
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- name: edit |
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dtype: string |
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- name: invert |
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dtype: string |
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- name: local |
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dtype: bool |
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- name: id |
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dtype: int32 |
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splits: |
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- name: test |
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num_bytes: 4106538055.5 |
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num_examples: 1277 |
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download_size: 703956134 |
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dataset_size: 4106538055.5 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/train-* |
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task_categories: |
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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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- Exemplar |
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- Editing |
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- Image2Image |
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- Diffusion |
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pretty_name: Top-Bench-X |
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size_categories: |
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- 1K<n<10K |
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--- |
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# EditCLIP: Representation Learning for Image Editing |
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<div> |
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[](https://arxiv.org/abs/2503.20318) |
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[](https://qianwangx.github.io/EditCLIP/) |
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[](https://github.com/QianWangX/EditCLIP) |
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[](https://iccv2025.thecvf.com/) |
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<!-- [](https://iccv2025.thecvf.com/) |
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[](https://iccv2025.thecvf.com/) --> |
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<!-- [📑 Paper](https://arxiv.org/abs/2503.20318) |
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[💻 Project Page](https://qianwangx.github.io/EditCLIP/) |
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[🐙 Github](https://github.com/QianWangX/EditCLIP) |
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[](https://iccv2025.thecvf.com/) --> |
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</div> |
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## 📚 Introduction |
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The **TOP-Bench-X** dataset offers **Query** and **Exemplar** image pairs tailored for exemplar-based image editing. We built it by adapting the TOP-Bench dataset from [InstructBrush](https://royzhao926.github.io/InstructBrush/) (also uploaded huggingface version at [Aleksandar/InstructBrush-Bench](https://huggingface.co/datasets/Aleksandar/InstructBrush-Bench)). Specifically, we use the original training split to generate exemplar images and the test split to supply their corresponding queries. In total, TOP-Bench-X comprises **1,277** samples, including **257** distinct exemplars and **124** unique queries. |
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<img src="assets/teaser_editclip.png" alt="Teaser figure of EditCLIP" width="100%"> |
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## 💡 Abstract |
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We introduce EditCLIP, a novel representation-learning approach for image editing. Our method learns a unified representation of edits by jointly encoding an input image and its edited counterpart, effectively capturing their transformation. To evaluate its effectiveness, we employ EditCLIP to solve two tasks: exemplar-based image editing and automated edit evaluation. In exemplar-based image editing, we replace text-based instructions in InstructPix2Pix with EditCLIP embeddings computed from a reference exemplar image pair. Experiments demonstrate that our approach outperforms state-of-the-art methods while being more efficient and versatile. For automated evaluation, EditCLIP assesses image edits by measuring the similarity between the EditCLIP embedding of a given image pair and either a textual editing instruction or the EditCLIP embedding of another reference image pair. Experiments show that EditCLIP aligns more closely with human judgments than existing CLIP-based metrics, providing a reliable measure of edit quality and structural preservation. |
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## 🧠 Data explained |
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Each sample consists of 4 images (2 pairs of images) and metadata, specifically: |
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1. *input_test* – the query image \(I_q\) from the test split (“before” image you want to edit) |
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2. *input_gt* – the ground-truth edited version of that query image (“after” image for the test) |
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3. *exemplar_input* – the exemplar’s input image \(I_i\) from the training split (“before” image of the exemplar) |
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4. *exemplar_edit* – the exemplar’s edited image \(I_e\) from the training split (“after” image of the exemplar) |
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## 🌟 Citation |
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```bibtex |
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@article{wang2025editclip, |
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title={EditCLIP: Representation Learning for Image Editing}, |
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author={Wang, Qian and Cvejic, Aleksandar and Eldesokey, Abdelrahman and Wonka, Peter}, |
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journal={arXiv preprint arXiv:2503.20318}, |
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year={2025} |
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} |
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``` |
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## 💳 License |
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This dataset is mainly a variation of TOP-Bench, confirm the license from the original authors. |
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