论文标题
具有深度级别SVM的相对属性分类
Relative Attribute Classification with Deep Rank SVM
论文作者
论文摘要
相对属性表示图像对之间特定属性的强度。我们引入了一个具有等级SVM损耗函数的Deep Siamese网络，称为Deep Rank SVM（DRSVM），以确定哪一对图像中的哪一个具有更强的特定属性。该网络以端到端的方式进行培训，以共同学习视觉功能和排名功能。我们证明了在四个图像基准数据集上针对最新方法的方法的有效性：LFW-10，PubFig，UTZAP50K-LEXI和UTZAP50K-2数据集。 DRSVM在四个图像基准数据集中的三个上，就属性的平均准确性超过了最先进的方法。
Relative attributes indicate the strength of a particular attribute between image pairs. We introduce a deep Siamese network with rank SVM loss function, called Deep Rank SVM (DRSVM), in order to decide which one of a pair of images has a stronger presence of a specific attribute. The network is trained in an end-to-end fashion to jointly learn the visual features and the ranking function. We demonstrate the effectiveness of our approach against the state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig, UTZap50K-lexi and UTZap50K-2 datasets. DRSVM surpasses state-of-art in terms of the average accuracy across attributes, on three of the four image benchmark datasets.
