{
  "id": "2003.04094",
  "version": "v1",
  "published": "2020-03-09T12:50:15.000Z",
  "updated": "2020-03-09T12:50:15.000Z",
  "title": "A Strong Baseline for Fashion Retrieval with Person Re-Identification Models",
  "authors": [
    "Mikolaj Wieczorek",
    "Andrzej Michalowski",
    "Anna Wroblewska",
    "Jacek Dabrowski"
  ],
  "comment": "33 pages, 14 figures",
  "categories": [
    "cs.CV",
    "cs.IR"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-03-09T12:50:15.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "person re-identification models",
      "strong baseline",
      "conduct in-depth experiments",
      "state-of-the-art results despite",
      "fashion retrieval models"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 33,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}