Abstract
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.
This work was supported by the EU co-funded Smart Growth Operational Programme 2014–2020 (project no. POIR.01.01.01-00-0695/19).
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Wieczorek, M., Michalowski, A., Wroblewska, A., Dabrowski, J. (2020). A Strong Baseline for Fashion Retrieval with Person Re-identification Models. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_33
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DOI: https://doi.org/10.1007/978-3-030-63820-7_33
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