{"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/hypergraph-propagation-and-community","title":"Hypergraph Propagation and Community Selection for Objects Retrieval","arxiv_id":null,"date":"2021-12-01","proceeding":"NeurIPS 2021 12","authors":["Guoyuan An","Yuchi Huo","Sung-Eui Yoon"],"abstract":"Spatial verification is a crucial technique for particular object retrieval. It utilizes spatial information for the accurate detection of true positive images. However, existing query expansion and diffusion methods cannot efficiently propagate the spatial information in an ordinary graph with scalar edge weights, resulting in low recall or precision. To tackle these problems, we propose a novel hypergraph-based framework that efficiently propagates spatial information in query time and retrieves an object in the database accurately. Additionally, we propose using the image graph's structure information through community selection technique, to measure the accuracy of the initial search result and to provide correct starting points for hypergraph propagation without heavy spatial verification computations. Experiment results on ROxford and RParis show that our method  significantly outperforms the existing query expansion and diffusion methods.","url_abs":"http://proceedings.neurips.cc/paper/2021/hash/1da546f25222c1ee710cf7e2f7a3ff0c-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2021/file/1da546f25222c1ee710cf7e2f7a3ff0c-Paper.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":"hypergraph-propagation-and-community","repo_url":"https://github.com/anguoyuan/Hypergraph-Propagation-and-Community-Selection-for-Objects-Retrieval","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"object","task_name":"Object"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-retrieval-on-roxford-hard","task":"Image Retrieval","dataset":"ROxford (Hard)","model":"Hypergraph propagation+community selection","rank_in_archive_order":3,"of":23,"metrics":{"mAP":"73"},"uses_additional_data":false},{"leaderboard":"/sota/image-retrieval-on-roxford-medium","task":"Image Retrieval","dataset":"ROxford (Medium)","model":"Hypergraph propagation+Community selection","rank_in_archive_order":2,"of":23,"metrics":{"mAP":"88.4"},"uses_additional_data":false},{"leaderboard":"/sota/image-retrieval-on-rparis-hard","task":"Image Retrieval","dataset":"RParis (Hard)","model":"Hypergraph propagation","rank_in_archive_order":3,"of":23,"metrics":{"mAP":"83.3"},"uses_additional_data":false},{"leaderboard":"/sota/image-retrieval-on-rparis-medium","task":"Image Retrieval","dataset":"RParis (Medium)","model":"Hypergraph propagation","rank_in_archive_order":2,"of":23,"metrics":{"mAP":"92.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}