Delta-Adapter：基于单对监督的可扩展示例图像编辑
Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision
AI总结：
Delta-Adapter通过单对监督学习可迁移的编辑语义，利用预训练视觉编码器提取语义差异，并通过Perceiver-based适配器注入图像编辑模型，提升编辑准确性和内容一致性。
摘要：
Delta-Adapter通过单对监督学习可迁移的编辑语义，利用预训练视觉编码器提取语义差异，并通过Perceiver-based适配器注入图像编辑模型，提升编辑准确性和内容一致性。
英文摘要：
Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics under single-pair supervision, requiring no textual guidance. Rather than directly exposing the exemplar pair to the model, we leverage a pre-trained vision encoder to extract a semantic delta that encodes the visual transformation between the two images. This semantic delta is injected into a pre-trained image editing model via a Perceiver-based adapter. Since the target image is never directly visible to the model, it can serve as the prediction target, enabling single-pair supervision without requiring additional exemplar pairs. This formulation allows us to leverage existing large-scale editing datasets for training. To further promote faithful transformation transfer, we introduce a semantic delta consistency loss that aligns the semantic change of the generated output with the ground-truth semantic delta extracted from the exemplar pair. Extensive experiments demonstrate that Delta-Adapter consistently improves both editing accuracy and content consistency over four strong baselines on seen editing tasks, while also generalizing more effectively to unseen editing tasks. Code will be available at https://delta-adapter.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
