Relative attribute models can compare images in terms of all detected
properties or attributes, exhaustively predicting which image is fancier, more
natural, and so on without any regard to ordering. However, when humans compare
images, certain differences will naturally stick out and come to mind first.
These most noticeable differences, or prominent differences, are likely to be
described first. In addition, many differences, although present, may not be
mentioned at all. In this work, we introduce and model prominent differences, a
rich new functionality for comparing images. We collect instance-level
annotations of most noticeable differences, and build a model trained on
relative attribute features that predicts prominent differences for unseen
pairs. We test our model on the challenging UT-Zap50K shoes and LFW10 faces
datasets, and outperform an array of baseline methods. We then demonstrate how
our prominence model improves two vision tasks, image search and description
generation, enabling more natural communication between people and vision
systems.

此篇论文探讨了突出不同之处的新功能，使用相对属性特征预测视觉上最显著的差异，并在 UT-Zap50K 鞋和 LFW10 人脸数据集上进行了测试，证明了改进图像搜索和描述生成任务的性能。