An Effective Pipeline for a Real-world Clothes Retrieval System

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Authors Jongtack Kim, Hyong-Keun Kook, Byungsoo Ko, Jingeun Lee, Sanghyuk Park, Youngjoon Kim, Insik Kim, HeeJae Jun, Sangwon Lee, Yang-Ho Ji
Journal/Conference Name arXiv preprint
Paper Category
Paper Abstract In this paper, we propose an effective pipeline for clothes retrieval system which has sturdiness on large-scale real-world fashion data. Our proposed method consists of three components detection, retrieval, and post-processing. We firstly conduct a detection task for precise retrieval on target clothes, then retrieve the corresponding items with the metric learning-based model. To improve the retrieval robustness against noise and misleading bounding boxes, we apply post-processing methods such as weighted boxes fusion and feature concatenation. With the proposed methodology, we achieved 2nd place in the DeepFashion2 Clothes Retrieval 2020 challenge.
Date of publication 2020
Code Programming Language Unspecified
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