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

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.

33 pages, 14 figures

References in corpus (6)

• A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification
• Deep Transfer Learning for Person Re-identification
• Learning Generalisable Omni-Scale Representations for Person Re-Identification
• DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
• MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
• The iMaterialist Fashion Attribute Dataset