{"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/learning-to-learn-relation-for-important","title":"Learning to Learn Relation for Important People Detection in Still Images","arxiv_id":"1904.03632","date":"2019-04-07","proceeding":"CVPR 2019 6","authors":["Wei-Hong Li","Fa-Ting Hong","Wei-Shi Zheng"],"abstract":"Humans can easily recognize the importance of people in social event images,\nand they always focus on the most important individuals. However, learning to\nlearn the relation between people in an image, and inferring the most important\nperson based on this relation, remains undeveloped. In this work, we propose a\ndeep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling\nand feature learning. In particular, we infer two types of interaction modules:\nthe person-person interaction module that learns the interaction between people\nand the event-person interaction module that learns to describe how a person is\ninvolved in the event occurring in an image. We then estimate the importance\nrelations among people from both interactions and encode the relation feature\nfrom the importance relations. In this way, POINT automatically learns several\ntypes of relation features in parallel, and we aggregate these relation\nfeatures and the person's feature to form the importance feature for important\npeople classification. Extensive experimental results show that our method is\neffective for important people detection and verify the efficacy of learning to\nlearn relations for important people detection.","url_abs":"http://arxiv.org/abs/1904.03632v1","url_pdf":"http://arxiv.org/pdf/1904.03632v1.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":"learning-to-learn-relation-for-important","repo_url":"https://github.com/DorBernsohn/TrainingDynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-network","task_name":"Relation Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03632","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}