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

Fashion retrieval is a 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 and catalogue photos, respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists. 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 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. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.