EditCLIP: Representation Learning for Image Editing

• URL: http://arxiv.org/abs/2503.20318v1
• Date: Wed, 26 Mar 2025 08:36:25 GMT
• Title: EditCLIP: Representation Learning for Image Editing
• Authors: Qian Wang, Aleksandar Cvejic, Abdelrahman Eldesokey, Peter Wonka,
• Abstract summary: We introduce EditCLIP, a representation-learning approach for image editing.<n>For exemplar-based image editing, we replace text-based instructions in InstructPix2Pix with EditCLIP embeddings computed from a reference exemplar image pair.<n>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.
• Score: 80.90787415853626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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