We propose a novel problem revolving around two tasks: (i) given a scene, recommend objects to insert, and (ii) given an object category, retrieve suitable background scenes. A bounding box for the inserted object is predicted in both tasks, which helps downstream applications such as semiautomated advertising and video composition. The major challenge lies in the fact that the target object is neither present nor localized in the input, and furthermore, available datasets only provide scenes with existing objects. To tackle this problem, we build an unsupervised algorithm based on object-level contexts, which explicitly models the joint probability distribution of object categories and bounding boxes using a Gaussian mixture model. Experiments on our own annotated test set demonstrate that our system outperforms existing baselines on all sub-tasks, and does so using a unified framework. Future extensions and applications are suggested.

中文翻译：

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内容和地点：用于对象插入的基于上下文的推荐系统

我们提出了一个围绕两个任务的新颖问题：（i）给定场景，推荐要插入的对象，（ii）给定对象类别，检索合适的背景场景。在两个任务中都将预测插入对象的边界框，这有助于下游应用程序，例如半自动广告和视频合成。主要的挑战在于以下事实：目标对象既不在输入中也不存在，也无法在输入中定位，此外，可用的数据集仅提供具有现有对象的场景。为了解决这个问题，我们建立了一个基于对象级上下文的无监督算法，该算法使用高斯混合模型显式地建模对象类别和边界框的联合概率分布。在我们自己的带注释的测试集上进行的实验表明，我们的系统在所有子任务上的性能均优于现有基准，并且使用统一框架进行了测试。建议将来的扩展和应用。