用于自监督文本引导图像编辑的提示增强
Prompt Augmentation for Self-supervised Text-guided Image Manipulation
AI总结：
本文提出提示增强方法，将单一提示扩展为多个目标提示以强化文本上下文，并设计软对比损失，在扩散模型中实现局部图像编辑，取得优于或媲美现有方法的性能。
摘要：
文本引导的图像编辑在创意和实际领域有着广泛的应用。尽管近期图像生成研究推动了该领域的发展，但这些方法往往难以同时应对图像连贯变换与上下文保留的双重挑战。为此，我们的工作引入了提示增强方法，该方法将单个输入提示扩展为多个目标提示，以强化文本上下文并实现局部图像编辑。具体而言，我们利用增强后的提示来划定预期的操作区域。我们提出了一种对比损失，通过位移编辑区域并拉近保留区域，以驱动有效的图像编辑。考虑到图像编辑的连续性，我们进一步引入相似性概念来优化方法，形成软对比损失。新损失被整合到扩散模型中，在公开数据集和生成图像上展示了相较于最先进方法更优或相当的结果。
英文摘要：
Text-guided image editing finds applications in various creative and practical fields. While recent studies in image generation have advanced the field, they often struggle with the dual challenges of coherent image transformation and context preservation. In response, our work introduces prompt augmentation, a method amplifying a single input prompt into several target prompts, strengthening textual context and enabling localised image editing. Specifically, we use the augmented prompts to delineate the intended manipulation area. We propose a Contrastive Loss tailored to driving effective image editing by displacing edited areas and drawing preserved regions closer. Acknowledging the continuous nature of image manipulations, we further refine our approach by incorporating the similarity concept, creating a Soft Contrastive Loss. The new losses are incorporated to the diffusion model, demonstrating improved or competitive image editing results on public datasets and generated images over state-of-the-art approaches.
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