A Multi-staged Feature-Attentive Network for Fashion Clothing Classification and Attribute Prediction

In the visual fashion clothing analysis, many researchers are attracted with the success of deep learning concepts. In this work, we introduce a multi-staged feature-attentive network to attain clothing category classification and attribute prediction. The proposed network in this work brings out a landmark-independent structure, whereas the existing landmark-dependent structures take up a lot of manpower for landmark annotation and also suffers from inter- and intra-individual variability. Our focus on this work is intensifying feature extraction by incorporating low-level and high-level feature fusion within fashion network. We are aiming on multi-level contextual features which utilise spatial and channel-wise information to create contextual feature supervision. Further, we enclose a semi-supervised learning approach to escalate fashion clothes analysis that utilises knowledge sharing among labelled and unlabelled data. To the best of our knowledge, this is the first attempt to investigate the semi-supervised learning in fashion clothing analysis by adopting multitask architecture which simultaneously study the clothing categories as well as its attributes. We evaluated the proposed approach on large-scale DeepFashion-C dataset while unlabelled dataset obtained from six publicly available fashion datasets. Experimental results show that the proposed architectures for supervised and semi-supervised learning entailing deep convolutional neural network outperforms many state-of-the-art techniques considerably, in fashion clothing analysis