Progressive Fashion Attribute Extraction

Autor:	Adhikari, Sandeep Singh, Singh, Sukhneer, Rajagopal, Anoop, Rajan, Aruna
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	Extracting fashion attributes from images of people wearing clothing/fashion accessories is a very hard multi-class classification problem. Most often, even catalogues of fashion do not have all the fine-grained attributes tagged due to prohibitive cost of annotation. Using images of fashion articles, running multi-class attribute extraction with a single model for all kinds of attributes (neck design detailing, sleeves detailing, etc) requires classifiers that are robust to missing and ambiguously labelled data. In this work, we propose a progressive training approach for such multi-class classification, where weights learnt from an attribute are fine tuned for another attribute of the same fashion article (say, dresses). We branch networks for each attributes from a base network progressively during training. While it may have many labels, an image doesn't need to have all possible labels for fashion articles present in it. We also compare our approach to multi-label classification, and demonstrate improvements over overall classification accuracies using our approach. Comment: 6 pages, 6 figures, AI for fashion : KDD 2019 Workshop, August 2019, Anchorage, Alaska - USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.00157 Zobrazit plný text záznamu View this record from Arxiv