{"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/fusing-hierarchical-convolutional-features","title":"Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification","arxiv_id":"1803.03415","date":"2018-03-09","proceeding":null,"authors":["Zheng Zhang","Chengfang Song","Qin Zou"],"abstract":"The clothing fashion reflects the common aesthetics that people share with\neach other in dressing. To recognize the fashion time of a clothing is\nmeaningful for both an individual and the industry. In this paper, under the\nassumption that the clothing fashion changes year by year, the fashion-time\nrecognition problem is mapped into a clothing-fashion classification problem.\nSpecifically, a novel deep neural network is proposed which achieves accurate\nhuman body segmentation by fusing multi-scale convolutional features in a fully\nconvolutional network, and then feature learning and fashion classification are\nperformed on the segmented parts avoiding the influence of image background. In\nthe experiments, 9,339 fashion images from 8 continuous years are collected for\nperformance evaluation. The results demonstrate the effectiveness of the\nproposed body segmentation and fashion classification methods.","url_abs":"http://arxiv.org/abs/1803.03415v2","url_pdf":"http://arxiv.org/pdf/1803.03415v2.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":"fusing-hierarchical-convolutional-features","repo_url":"https://github.com/AemikaChow/DATASOURCE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}