论文标题
核心：多色时尚服装的颜色回归
CoRe: Color Regression for Multicolor Fashion Garments
论文作者
论文摘要
开发分析时尚服装的深层网络具有许多真实的应用程序。在所有时尚属性中，颜色是检测到的最重要但最具挑战性的一种。现有方法是基于分类的，因此不能超出离散预定义颜色名称的列表。在本文中，我们将颜色检测视为回归问题，以预测精确的RGB值。这就是为什么除了第一个颜色分类器之外，我们还包括第二个回归阶段，以在新提出的架构中进行完善。第二步结合了两个注意力模型：第一个取决于衣服的类型，第二个取决于分类器先前检测到的颜色。我们的最终预测是对图像像素RGB值的加权空间合并，在该值已纠正了照明。该体系结构是模块化的，很容易扩展，以检测多色服装中所有颜色的RGB。在我们的实验中，我们展示了架构的每个组成部分的好处。
Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.
