TY - GEN
T1 - A Strong Baseline for Fashion Retrieval with Person Re-identification Models
AU - Wieczorek, Mikolaj
AU - Michalowski, Andrzej
AU - Wroblewska, Anna
AU - Dabrowski, Jacek
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Clothes retrieval
KW - Deep learning in fashion
KW - Fashion retrieval
KW - Person re-identification
KW - Quadruplet loss
UR - http://www.scopus.com/inward/record.url?scp=85097288771&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63820-7_33
DO - 10.1007/978-3-030-63820-7_33
M3 - Conference contribution
AN - SCOPUS:85097288771
SN - 9783030638191
T3 - Communications in Computer and Information Science
SP - 294
EP - 301
BT - Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
A2 - Yang, Haiqin
A2 - Pasupa, Kitsuchart
A2 - Leung, Andrew Chi-Sing
A2 - Kwok, James T.
A2 - Chan, Jonathan H.
A2 - King, Irwin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Neural Information Processing, ICONIP 2020
Y2 - 18 November 2020 through 22 November 2020
ER -