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。

无约束前景目标搜索

Unconstrained Foreground Object Search

Cross-modal similarity search is a problem about designing a search system
supporting querying across content modalities, e.g., using an image to search
for texts or using a text to search for images. This paper presents a compact
coding solution for efficient search, with a focus on the quantization approach
which has already shown the superior performance over the hashing solutions in
the single-modal similarity search. We propose a cross-modal quantization
approach, which is among the early attempts to introduce quantization into
cross-modal search. The major contribution lies in jointly learning the
quantizers for both modalities through aligning the quantized representations
for each pair of image and text belonging to a document. In addition, our
approach simultaneously learns the common space for both modalities in which
quantization is conducted to enable efficient and effective search using the
Euclidean distance computed in the common space with fast distance table
lookup. Experimental results compared with several competitive algorithms over
three benchmark datasets demonstrate that the proposed approach achieves the
state-of-the-art performance.

本文给出了一种紧凑的编码方案，使用量化方法实现跨模态搜索，通过联合学习图像和文本的量化器来实现。经过实验证明，该方法在三个基准数据集上实现了最先进的性能表现。