Relative Attributes Classification via Transformers and Rank SVM Loss

We propose a new model for learning to rank two images with respect to their relative strength of expression for a given attribute. We address this problem - called relative attribute learning - using a vision transformer backbone. The embedded representations of the two images to be compared are extracted and used for comparison with a ranking head, in an end-to-end fashion. The results demonstrate the strength of vision transformers and their suitability for relative attributes classification. Our proposed approach outperforms the state-of-the-art by a large margin, achieving 90.40% and 98.14% mean accuracy over the attributes of LFW-10 and Pubfig datasets.