{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-learning-based-fashion-attributes","title":"A Deep-Learning-Based Fashion Attributes Detection Model","arxiv_id":"1810.10148","date":"2018-10-24","proceeding":null,"authors":["Menglin Jia","Yichen Zhou","Mengyun Shi","Bharath Hariharan"],"abstract":"Analyzing fashion attributes is essential in the fashion design process.\nCurrent fashion forecasting firms, such as WGSN utilizes information from all\naround the world (from fashion shows, visual merchandising, blogs, etc). They\ngather information by experience, by observation, by media scan, by interviews,\nand by exposed to new things. Such information analyzing process is called\nabstracting, which recognize similarities or differences across all the\ngarments and collections. In fact, such abstraction ability is useful in many\nfashion careers with different purposes. Fashion forecasters abstract across\ndesign collections and across time to identify fashion change and directions;\ndesigners, product developers and buyers abstract across a group of garments\nand collections to develop a cohesive and visually appeal lines; sales and\nmarketing executives abstract across product line each season to recognize\nselling points; fashion journalist and bloggers abstract across runway photos\nto recognize symbolic core concepts that can be translated into editorial\nfeatures. Fashion attributes analysis for such fashion insiders requires much\ndetailed and in-depth attributes annotation than that for consumers, and\nrequires inference on multiple domains. In this project, we propose a\ndata-driven approach for recognizing fashion attributes. Specifically, a\nmodified version of Faster R-CNN model is trained on images from a large-scale\nlocalization dataset with 594 fine-grained attributes under different\nscenarios, for example in online stores and street snapshots. This model will\nthen be used to detect garment items and classify clothing attributes for\nrunway photos and fashion illustrations.","url_abs":"http://arxiv.org/abs/1810.10148v1","url_pdf":"http://arxiv.org/pdf/1810.10148v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-deep-learning-based-fashion-attributes","repo_url":"https://github.com/ekolodyazhnay/test-ds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"marketing","task_name":"Marketing"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}