面向小物体编辑：一个基准数据集与免训练方法
Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
AI总结：
本文提出免训练注意力引导方法解决小物体编辑中的跨模态对齐问题，并构建SOEBench基准，显著提升生成保真度与准确性。
摘要：
近年来，通过利用大规模扩散生成模型（尤其是Stable Diffusion）的卓越能力，已经开发出大量文本引导的图像编辑方法。尽管扩散模型在生成高质量图像方面取得了成功，但由于难以对齐文本与这些小物体之间的跨模态注意力图，其在小物体生成中的应用受到限制。我们的方法提供了一种免训练方案，通过局部和全局注意力引导显著缓解了这一对齐问题，增强了模型根据文本描述准确渲染小物体的能力。我们详细阐述了该方法，强调其与传统生成技术的不同之处，并突出其优势。更重要的是，我们还提供了SOEBench（小物体编辑），这是一个从MSCOCO和OpenImage收集的标准化基准，用于定量评估基于文本的小物体生成。初步结果表明了我们的方法的有效性，与现有模型相比，在小物体生成的保真度和准确性方面显示出显著改进。这一进展不仅为人工智能和计算机视觉领域做出了贡献，还为需要精确图像生成的各种行业应用开辟了新的可能性。我们将在项目页面发布数据集：https://soebench.github.io/。
英文摘要：
A plethora of text-guided image editing methods has recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models especially Stable Diffusion. Despite the success of diffusion models in producing high-quality images, their application to small object generation has been limited due to difficulties in aligning cross-modal attention maps between text and these objects. Our approach offers a training-free method that significantly mitigates this alignment issue with local and global attention guidance , enhancing the model's ability to accurately render small objects in accordance with textual descriptions. We detail the methodology in our approach, emphasizing its divergence from traditional generation techniques and highlighting its advantages. What's more important is that we also provide~\textit{SOEBench} (Small Object Editing), a standardized benchmark for quantitatively evaluating text-based small object generation collected from \textit{MSCOCO} and \textit{OpenImage}. Preliminary results demonstrate the effectiveness of our method, showing marked improvements in the fidelity and accuracy of small object generation compared to existing models. This advancement not only contributes to the field of AI and computer vision but also opens up new possibilities for applications in various industries where precise image generation is critical. We will release our dataset on our project page: \href{https://soebench.github.io/}{https://soebench.github.io/}.
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。
