{"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/what-looks-good-with-my-sofa-multimodal","title":"What Looks Good with my Sofa: Multimodal Search Engine for Interior Design","arxiv_id":"1707.06907","date":"2017-07-21","proceeding":null,"authors":["Ivona Tautkute","Aleksandra Możejko","Wojciech Stokowiec","Tomasz Trzciński","Łukasz Brocki","Krzysztof Marasek"],"abstract":"In this paper, we propose a multi-modal search engine for interior design\nthat combines visual and textual queries. The goal of our engine is to retrieve\ninterior objects, e.g. furniture or wall clocks, that share visual and\naesthetic similarities with the query. Our search engine allows the user to\ntake a photo of a room and retrieve with a high recall a list of items\nidentical or visually similar to those present in the photo. Additionally, it\nallows to return other items that aesthetically and stylistically fit well\ntogether. To achieve this goal, our system blends the results obtained using\ntextual and visual modalities. Thanks to this blending strategy, we increase\nthe average style similarity score of the retrieved items by 11%. Our work is\nimplemented as a Web-based application and it is planned to be opened to the\npublic.","url_abs":"http://arxiv.org/abs/1707.06907v2","url_pdf":"http://arxiv.org/pdf/1707.06907v2.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":"what-looks-good-with-my-sofa-multimodal","repo_url":"https://github.com/peter0083/DeepDeco","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}