InstructBrush：学习基于注意力的指令优化以用于图像编辑
InstructBrush: Learning Attention-based Instruction Optimization for Image Editing
AI总结：
提出InstructBrush反演方法，通过基于注意力的指令优化和面向转换的指令初始化技术，从示例图像对中提取编辑指令，并建立TOP-Bench基准，显著提升了图像编辑的性能与语义一致性。
摘要：
近年来，基于指令的图像编辑方法在图像编辑领域引起了广泛关注。然而，尽管涵盖了广泛的编辑先验，这些方法在处理难以通过语言准确描述的编辑任务时仍显得无能为力。我们提出了InstructBrush，一种用于基于指令的图像编辑方法的反演方法，以弥合这一差距。它从示例图像对中提取编辑效果作为编辑指令，并将其进一步应用于图像编辑。InstructBrush中引入了两个关键技术，即基于注意力的指令优化和面向转换的指令初始化，以解决先前方法在反演效果和指令泛化方面的局限性。为了探索指令反演方法在开放场景中指导图像编辑的能力，我们建立了一个面向转换的配对基准（TOP-Bench），其中包含丰富的场景和编辑类型。该基准的创建为进一步探索指令反演铺平了道路。定量和定性实验表明，我们的方法在编辑中取得了优越的性能，并且在语义上与目标编辑效果更加一致。
英文摘要：
In recent years, instruction-based image editing methods have garnered significant attention in image editing. However, despite encompassing a wide range of editing priors, these methods are helpless when handling editing tasks that are challenging to accurately describe through language. We propose InstructBrush, an inversion method for instruction-based image editing methods to bridge this gap. It extracts editing effects from exemplar image pairs as editing instructions, which are further applied for image editing. Two key techniques are introduced into InstructBrush, Attention-based Instruction Optimization and Transformation-oriented Instruction Initialization, to address the limitations of the previous method in terms of inversion effects and instruction generalization. To explore the ability of instruction inversion methods to guide image editing in open scenarios, we establish a TransformationOriented Paired Benchmark (TOP-Bench), which contains a rich set of scenes and editing types. The creation of this benchmark paves the way for further exploration of instruction inversion. Quantitatively and qualitatively, our approach achieves superior performance in editing and is more semantically consistent with the target editing effects.
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。
