MDE-Edit：通过扩散模型进行多对象图像编辑的掩码双重编辑
MDE-Edit: Masked Dual-Editing for Multi-Object Image Editing via Diffusion Models
AI总结：
针对复杂场景中多对象编辑的定位偏差与注意力泄漏问题，本文提出无需训练的 MDE-Edit，通过 OAL 与 CCL 双重损失优化扩散潜变量，实现更准确、连贯的局部编辑。
摘要：
多对象编辑旨在修改复杂场景中的多个对象或区域，同时保持结构一致性。该任务在涉及重叠或交互对象的场景中面临重大挑战：（1）由于注意力错位导致目标对象定位不准确，从而产生不完整或位置错误的编辑；（2）属性-对象不匹配，即颜色或纹理变化因交叉注意力泄漏而无法与预期区域对齐，造成语义冲突（例如，颜色渗入非目标区域）。现有方法难以应对这些挑战：依赖全局交叉注意力机制的方法会受到注意力稀释和对象间空间干扰的影响，而基于掩码的方法由于多对象场景中的特征纠缠，无法将属性绑定到几何上准确的区域。为解决这些局限，我们提出了一种无需训练、处于推理阶段的优化方法，可在复杂多对象场景中实现精确的局部图像操控，名为 MDE-Edit。MDE-Edit 通过两个关键损失优化扩散模型中的噪声潜在特征：对象对齐损失（OAL）将多层交叉注意力与分割掩码对齐，以精确定位对象；颜色一致性损失（CCL）增强掩码内的目标属性注意力，同时抑制向相邻区域的泄漏。这种双重损失设计确保了局部且连贯的多对象编辑。大量实验表明，MDE-Edit 在编辑准确性和视觉质量方面优于最先进方法，为复杂的多对象图像操控任务提供了稳健解决方案。
英文摘要：
Multi-object editing aims to modify multiple objects or regions in complex scenes while preserving structural coherence. This task faces significant challenges in scenarios involving overlapping or interacting objects: (1) Inaccurate localization of target objects due to attention misalignment, leading to incomplete or misplaced edits; (2) Attribute-object mismatch, where color or texture changes fail to align with intended regions due to cross-attention leakage, creating semantic conflicts (\textit{e.g.}, color bleeding into non-target areas). Existing methods struggle with these challenges: approaches relying on global cross-attention mechanisms suffer from attention dilution and spatial interference between objects, while mask-based methods fail to bind attributes to geometrically accurate regions due to feature entanglement in multi-object scenarios. To address these limitations, we propose a training-free, inference-stage optimization approach that enables precise localized image manipulation in complex multi-object scenes, named MDE-Edit. MDE-Edit optimizes the noise latent feature in diffusion models via two key losses: Object Alignment Loss (OAL) aligns multi-layer cross-attention with segmentation masks for precise object positioning, and Color Consistency Loss (CCL) amplifies target attribute attention within masks while suppressing leakage to adjacent regions. This dual-loss design ensures localized and coherent multi-object edits. Extensive experiments demonstrate that MDE-Edit outperforms state-of-the-art methods in editing accuracy and visual quality, offering a robust solution for complex multi-object image manipulation tasks.
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。
