超越文本思维链：深度融合置信度推理的交错文本-图像链用于图像编辑
Beyond Textual CoT: Interleaved Text-Image Chains with Deep Confidence Reasoning for Image Editing
AI总结：
针对现有图像编辑缺乏显式推理的问题，提出MURE框架，采用交错文本-图像思维链及多模态深度置信推理，分解复杂任务，提升编辑精度，并在三个基准上取得显著改进。
摘要：
基于自然语言的图像编辑已获得显著流行，然而现有方法在处理复杂的物体交叉和细粒度空间关系时存在困难，原因是缺乏显式的推理过程。尽管思维链（CoT）已被探索用于增强推理，但纯文本的CoT或辅以坐标信息的CoT在表示复杂视觉布局方面存在根本性局限，并且缺乏必要的视觉线索来指导细粒度、像素级细节的生成。为解决这些挑战，我们提出了多模态推理编辑（MURE），一种新颖的框架，将视觉编辑过程从纯文本推理转变为一系列交错的文本和视觉推理链。我们的框架使用原生多模态、交错的文本-图像CoT执行图像编辑。该方法生成逐步推理链，其中文本描述后跟随相应的视觉线索，例如定义预期编辑区域的位置掩码或新内容的表示。此外，为缓解大语言模型的幻觉现象，我们引入了多模态深度置信（MMDC）推理范式。该范式在每一步探索视觉推理路径树。通过使用来自奖励模型的深度置信分数剪除低质量分支，它确保模型始终遵循高质量轨迹以达成最终编辑结果。所提方法将复杂编辑任务分解为相互依赖的子任务，在每个阶段实现更高精度，并产生高保真编辑结果。我们定义了交错文本-图像链的公式，并发布了首个CoT-Edit-14K数据集，包含14K个高质量编辑示例。大量实验表明，我们的方法在三个图像编辑基准上取得了显著改进。
英文摘要：
Image editing with natural language has gained significant popularity, yet existing methods struggle with intricate object intersections and fine-grained spatial relationships due to the lack of an explicit reasoning process. While Chain-of-Thought (CoT) has been explored to enhance reasoning, purely textual CoT or CoT augmented with coordinate information is fundamentally limited in its ability to represent intricate visual layouts and lacks the necessary visual cues to guide the generation of fine-grained, pixel-level details. To address these challenges, we propose Multimodal Reasoning Edit (MURE), a novel framework that shifts the visual editing process from purely text-based reasoning to a series of interleaved textual and visual rationales. Our framework performs image editing using a natively multimodal, interleaved text-image CoT. This approach generates a step-by-step chain of reasoning where a textual description is followed by a corresponding visual cue, such as a positional mask that defined intended edited regions or a representation of new content. Furthermore, to mitigate the hallucination phenomenon of large language models, we introduce Multimodal Deep Confidence (MMDC) reasoning paradigm. This paradigm explores a tree of visual reasoning paths at each step. By pruning low-quality branches using a deep confidence score from a reward model, it ensures the model consistently follows a high-quality trajectory towards the final edited result. The proposed method decomposes complex editing tasks into interdependent sub-tasks, achieving greater precision at each stage and yielding high-fidelity edited results. We define the formulation for interleaved text-image chains and release the first CoT-Edit-14K dataset, comprising 14K high-quality editing examples. Extensive experiments show that our method yields significant improvements across three image editing benchmarks.
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