超越编辑对：基于多尺度可学习区域的细粒度指令图像编辑
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions
AI总结：
本文提出一种无需编辑对数据集、利用多尺度可学习区域定位编辑区域的指令图像编辑新范式，实现高保真、精确且与指令一致的编辑，并在多种任务和基准上达到最先进性能。
摘要：
当前基于文本驱动的图像编辑方法通常遵循两个方向之一：依赖大规模、高质量的编辑对数据集来提高编辑精度和多样性，或探索无需数据集的替代技术。然而，构建大规模编辑数据集需要精心设计的流程，耗时且常常导致不真实的样本或不需要的伪影。同时，无需数据集的方法可能面临指令理解有限和编辑能力受限的问题。面对这些挑战，本工作开发了一种用于指令驱动图像编辑的新范式，利用广泛可用且海量的文本-图像对，而非依赖编辑对数据集。我们的方法引入了一个多尺度可学习区域来定位和引导编辑过程。通过将图像与其文本描述之间的对齐作为监督，并学习生成任务特定的编辑区域，我们的方法实现了高保真、精确且与指令一致的图像编辑。大量实验表明，所提出的方法在各种任务和基准上达到了最先进的性能，同时展现出对各种类型生成模型的强适应性。
英文摘要：
Current text-driven image editing methods typically follow one of two directions: relying on large-scale, high-quality editing pair datasets to improve editing precision and diversity, or exploring alternative dataset-free techniques. However, constructing large-scale editing datasets requires carefully designed pipelines, is time-consuming, and often results in unrealistic samples or unwanted artifacts. Meanwhile, dataset-free methods may suffer from limited instruction comprehension and restricted editing capabilities. Faced with these challenges, the present work develops a novel paradigm for instruction-driven image editing that leverages widely available and enormous text-image pairs, instead of relying on editing pair datasets. Our approach introduces a multi-scale learnable region to localize and guide the editing process. By treating the alignment between images and their textual descriptions as supervision and learning to generate task-specific editing regions, our method achieves high-fidelity, precise, and instruction-consistent image editing. Extensive experiments demonstrate that the proposed approach attains state-of-the-art performance across various tasks and benchmarks, while exhibiting strong adaptability to various types of generative models.
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。
