研究人员正在开发新的图像编辑方法，超越传统的循序渐进生成。一种名为EAR的方法将视觉规划重新构建为单步转换，使用抽象谜题来测试推理能力。另一种方法Meta-CoT通过将任务分解为三元组和元任务来增强编辑，在粒度和泛化方面取得了显著改进。此外，一种新颖的训练范式允许图像编辑模型在没有配对数据的情况下进行优化，利用视觉语言模型的反馈来确保指令遵循和视觉保真度。 AI
影响 新的训练范式和模型架构有望实现更高效、更泛化的图像编辑能力。
排序理由 多篇研究论文发表在arXiv上，详细介绍了图像编辑的新方法和数据集。
AI 生成摘要 · Google Gemini · 来自 8 个来源。 我们如何撰写摘要 →
arXiv:2605.02290v1 Announce Type: new Abstract: Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches select complete reasoning traces post-hoc, overl…
arXiv:2604.25128v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-quality image generation, leading to increasing demand for post-generation editing that modifies local regions while preserving global structure. Achieving such flexible and prec…
arXiv:2604.24625v1 Announce Type: new Abstract: Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplo…
arXiv:2510.14978v2 Announce Type: replace Abstract: Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of input-target pairs. This is a critical bottleneck…
arXiv:2602.05676v2 Announce Type: replace Abstract: Recent advancements in 3D foundation models have enabled the generation of high-fidelity assets, yet precise 3D manipulation remains a significant challenge. Existing 3D editing frameworks often face a difficult trade-off betwee…
arXiv:2604.22868v1 Announce Type: new Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in machine learning, this inherently visual problem is often tackled through a verbal-…
Recent advances in diffusion models have enabled high-quality image generation, leading to increasing demand for post-generation editing that modifies local regions while preserving global structure. Achieving such flexible and precise editing requires a high-quality starting poi…
Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can…