Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem---how to accurately and robustly detect attributes from images---has been left under explored. Especially, the existing work rarely explicitly tackles the need that attribute detectors should generalize well across different categories, including those previously unseen. Noting that this is analogous to the objective of multi-source domain generalization, if we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multi-source domain generalization for the purpose of learning cross-category generalizable attribute detectors. We validate our understanding and approach with extensive experiments on four challenging datasets and three different problems.

本文探讨了如何从图像中准确地和鲁棒性地检测属性，并借鉴了多源域泛化的方法，为学习跨类别通用属性检测器提供了一种新的视角。经过对四个具有挑战性的数据集和三个不同问题的广泛实验验证了该方法的有效性。

学习属性等价于多源域泛化