A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

• URL: http://arxiv.org/abs/2003.04094v1
• Date: Mon, 9 Mar 2020 12:50:15 GMT
• Title: A Strong Baseline for Fashion Retrieval with Person Re-Identification Models
• Authors: Mikolaj Wieczorek (1), Andrzej Michalowski (1), Anna Wroblewska (1 and 2), Jacek Dabrowski (1) ((1) Synerise, (2) Warsaw University of Technology)
• Abstract summary: Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. Difficulties arise from the fine-grained nature of clothing items, very large intra-class and inter-class variance. Additionally, query and source images for the task usually come from different domains - street photos and catalogue photos respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists between domains. As a result, fashion retrieval is an active field of research both in academia and the industry. Inspired by recent advancements in Person Re-Identification research, we adapt leading ReID models to be used in fashion retrieval tasks. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results despite a much simpler architecture. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.

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