{"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/video2shop-exact-matching-clothes-in-videos","title":"Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images","arxiv_id":"1804.05287","date":"2018-04-14","proceeding":"CVPR 2017 7","authors":["Zhi-Qi Cheng","Xiao Wu","Yang Liu","Xian-Sheng Hua"],"abstract":"In recent years, both online retail and video hosting service are\nexponentially growing. In this paper, we explore a new cross-domain task,\nVideo2Shop, targeting for matching clothes appeared in videos to the exact same\nitems in online shops. A novel deep neural network, called AsymNet, is proposed\nto explore this problem. For the image side, well-established methods are used\nto detect and extract features for clothing patches with arbitrary sizes. For\nthe video side, deep visual features are extracted from detected object regions\nin each frame, and further fed into a Long Short-Term Memory (LSTM) framework\nfor sequence modeling, which captures the temporal dynamics in videos. To\nconduct exact matching between videos and online shopping images, LSTM hidden\nstates, representing the video, and image features, which represent static\nobject images, are jointly modeled under the similarity network with\nreconfigurable deep tree structure. Moreover, an approximate training method is\nproposed to achieve the efficiency when training. Extensive experiments\nconducted on a large cross-domain dataset have demonstrated the effectiveness\nand efficiency of the proposed AsymNet, which outperforms the state-of-the-art\nmethods.","url_abs":"http://arxiv.org/abs/1804.05287v2","url_pdf":"http://arxiv.org/pdf/1804.05287v2.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":"video2shop-exact-matching-clothes-in-videos","repo_url":"https://github.com/zhiqic/Video2ShopExactMatching","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"video2shop-exact-matching-clothes-in-videos","repo_url":"https://github.com/kyusbok/Video2ShopExactMatching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.05287","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05287"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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