A Strong Baseline For Fashion Retrieval With Person Re-identification Models
2020 · Mikolaj Wieczorek, Andrzej Michalowski, Anna Wroblewska, et al.
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
Authors
(none)
Tags
Stats
Related papers
- Training And Challenging Models For Text-guided Fashion Image Retrieval (2022)0.00
- Specialized Re-ranking: A Novel Retrieval-verification Framework For Cloth Changing Person Re-identification (2022)9.03
- Performance-efficiency Trade-off For Fashion Image Retrieval (2025)0.00
- Lookbench: A Live And Holistic Open Benchmark For Fashion Image Retrieval (2026)0.00
- An Effective Pipeline For A Real-world Clothes Retrieval System (2020)0.00
- Lrvs-fashion: Extending Visual Search With Referring Instructions (2023)0.00
- Studio2shop: From Studio Photo Shoots To Fashion Articles (2018)7.50
- Fashionbert: Text And Image Matching With Adaptive Loss For Cross-modal Retrieval (2020)15.16