标题： 常见补丁对象：上下文内外 显示英文标题

标题： Common Inpainted Objects In-N-Out of Context

摘要： 我们提出了Common Inpainted Objects In-N-Out of Context（COinCO），这是一个新型的数据集，旨在解决现有视觉数据集中缺乏离散情境例子的问题。 通过基于扩散模型的图像修复系统性地替换COCO图像中的物体，我们创建了97,722张独特的图像，这些图像既包括语境一致的场景也包括不一致的场景，从而实现有效的语境学习。 每个修复的物体都经过仔细验证，并通过多模态大型语言模型评估分类为在情境内或情境外。 我们的分析揭示了影响物体类别修复成功率的重要语义先验模式。 我们展示了COinCO支持的三个关键任务：（1）训练能够有效判断现有物体是否属于其语境的语境分类器；（2）一种新的从语境预测物体的任务，在实例和团簇级别确定哪些新物体自然适合给定场景；（3）在最先进的方法上进行增强语境的假检测，无需微调。 COinCO提供了一个具有语境变化的受控测试平台，为推进计算机视觉和图像取证中的语境感知视觉理解奠定了基础。 我们的代码和数据位于：https://github.com/YangTianze009/COinCO。 显示英文摘要

摘要： We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes, enabling effective context learning. Each inpainted object is meticulously verified and categorized as in- or out-of-context through a multimodal large language model assessment. Our analysis reveals significant patterns in semantic priors that influence inpainting success across object categories. We demonstrate three key tasks enabled by COinCO: (1) training context classifiers that effectively determine whether existing objects belong in their context; (2) a novel Objects-from-Context prediction task that determines which new objects naturally belong in given scenes at both instance and clique levels, and (3) context-enhanced fake detection on state-of-the-art methods without fine-tuning. COinCO provides a controlled testbed with contextual variations, establishing a foundation for advancing context-aware visual understanding in computer vision and image forensics. Our code and data are at: https://github.com/YangTianze009/COinCO.