Attentive Hierarchical Label Sharing for Enhanced Garment and Attribute Classification of Fashion Imagery

• Published in: Recommender Systems in Fashion and Retail pp. 95 - 115
• Main Authors: Papadopoulos, Stefanos-Iordanis, Koutlis, Christos, Sudheer, Manjunath, Pugliese, Martina, Rabiller, Delphine, Papadopoulos, Symeon, Kompatsiaris, Ioannis
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Electrical Engineering
• ISBN: 9783030940157; 3030940152
• ISSN: 1876-1100; 1876-1119
• DOI: 10.1007/978-3-030-94016-4_7

Fine-grained information extraction from fashion imagery is a challenging task due to the inherent diversity and complexity of fashion categories and attributes. Additionally, fashion imagery often depict multiple items while fashion items tend to follow hierarchical relations among various object types, categories, and attributes. In this study, we address both issues with a 2-step hierarchical deep learning pipeline consisting of (1) a low granularity object type detection module (upper body, lower body, full-body, footwear) and (2) two classification modules for garment categories and attributes based on the outcome of the first step. For the category and attribute-level classification stages, we examine a hierarchical label sharing (HLS) technique in two settings: (1) single-task learning (STL w/ HLS) and (2) multi-task learning with RNN and visual attention (MTL w/ RNN+VA). Our approach enables progressively focusing on appropriately detailed features for automatically learning the hierarchical relations of fashion and enabling predictions on images with complete outfits. Empirically, STL w/ HLS reached 93.99% top-3 accuracy while MTL w/ RNN+VA reached 97.57% top-5 accuracy for category classification on the DeepFashion benchmark, surpassing the current state of the art without requiring landmark or mask annotations nor specialized domain expertise.