Many people search for foreground objects to use when editing images. While
existing methods can retrieve candidates to aid in this, they are constrained
to returning objects that belong to a pre-specified semantic class. We instead
propose a novel problem of unconstrained foreground object (UFO) search and
introduce a solution that supports efficient search by encoding the background
image in the same latent space as the candidate foreground objects. A key
contribution of our work is a cost-free, scalable approach for creating a
large-scale training dataset with a variety of foreground objects of differing
semantic categories per image location. Quantitative and human-perception
experiments with two diverse datasets demonstrate the advantage of our UFO
search solution over related baselines.

本文提出了一种解决图像编辑中寻找前景对象的技术 —— 无约束前景物体搜索，并介绍了一种支持通过将背景图像与前景对象进行编码来进行高效搜索的解决方案。通过创建具有不同语义类别的多个前景对象的大型训练数据集，我们的无约束前景物体搜索解决方案优于相关基线，这在两个不同的数据集上进行了定量和人类感知实验的证实。