Browsing by Subject "visual fashion understanding"

Visual fashion understanding (VFU) is a discipline which aims to solve tasks related to clothing recognition, such as garment categorization, garment’s attributes prediction or clothes retrieval, with the use of computer vision algorithms trained on fashion-related data. Having surveyed VFU- related scientific literature, I conclude that, because of the fact that at the heart of all VFU tasks is the same issue of visually understanding garments, those VFU tasks are in fact related. I present a hypothesis that building larger multi-task learning models dedicated to predicting multiple VFU tasks at once might lead to better generalization properties of VFU models. I assess the validity of my hypothesis by implementing two deep learning solutions dedicated primarily to category and attribute prediction. First solution uses multi-task learning concept of sharing features from ad- ditional branch dedicated to localization task of landmarks’ position prediction. Second solution does not share knowledge from localization branch. Comparison of those two implementations con- firmed my hypothesis, as sharing knowledge between tasks increased category prediction accuracy by 53% and attributes prediction recall by 149%. I conclude that multi-task learning improves generalization properties of deep learning-based visual fashion understanding models across tasks.