Relative Attribute Classification with Deep Rank SVM
This work addresses the challenge of comparing attribute strengths between images for computer vision applications, representing an incremental improvement over existing methods.
The paper tackled the problem of relative attribute classification by introducing Deep Rank SVM (DRSVM), a deep Siamese network with rank SVM loss, which achieved state-of-the-art average accuracy on three out of four benchmark datasets.
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.