基于自动概念定位与遗忘的文本引导图像编辑
Text Guided Image Editing with Automatic Concept Locating and Forgetting
AI总结：
提出Locate and Forget (LaF)方法，通过比较句法树自动定位图像中待修改目标概念并遗忘其存在线索，解决了文本引导图像编辑中物体属性残留问题。
摘要：
随着文本引导的图像到图像扩散模型的发展，图像编辑取得了显著进展。然而，一个持续的挑战是如何在不依赖额外用户提供的引导下，根据文本指令将物体无缝融入图像。文本和图像本质上是不同的模态，这导致难以完全捕捉语言传达的语义意图并将其准确转化为期望的视觉修改。因此，文本引导的图像编辑模型常会生成残留物体属性的图像，未能完全符合人类预期。为解决这一挑战，模型应有效理解图像内容，避免提供的文本编辑提示与图像实际修改之间产生脱节。在本文中，我们提出了一种名为Locate and Forget (LaF) 的新方法，该方法通过比较目标提示的句法树和输入图像中的场景描述，有效定位图像中待修改的潜在目标概念，旨在让生成图像遗忘这些概念的存在线索。与基线方法相比，我们的方法在文本引导图像编辑任务中定性和定量地均展现出优越性。
英文摘要：
With the advancement of image-to-image diffusion models guided by text, significant progress has been made in image editing. However, a persistent challenge remains in seamlessly incorporating objects into images based on textual instructions, without relying on extra user-provided guidance. Text and images are inherently distinct modalities, bringing out difficulties in fully capturing the semantic intent conveyed through language and accurately translating that into the desired visual modifications. Therefore, text-guided image editing models often produce generations with residual object attributes that do not fully align with human expectations. To address this challenge, the models should comprehend the image content effectively away from a disconnect between the provided textual editing prompts and the actual modifications made to the image. In our paper, we propose a novel method called Locate and Forget (LaF), which effectively locates potential target concepts in the image for modification by comparing the syntactic trees of the target prompt and scene descriptions in the input image, intending to forget their existence clues in the generated image. Compared to the baselines, our method demonstrates its superiority in text-guided image editing tasks both qualitatively and quantitatively.
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。
