{"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/mason-a-model-agnostic-objectness-framework","title":"MASON: A Model AgnoStic ObjectNess Framework","arxiv_id":"1809.07499","date":"2018-09-20","proceeding":null,"authors":["K J Joseph","Vineeth N. Balasubramanian"],"abstract":"This paper proposes a simple, yet very effective method to localize dominant\nforeground objects in an image, to pixel-level precision. The proposed method\n'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to\ngenerate category-independent and model-agnostic heat maps for any image. The\nnetwork is not explicitly trained for the task, and hence, can be used\noff-the-shelf in tandem with any other network or task. We show that this\nframework scales to a wide variety of images, and illustrate the effectiveness\nof MASON in three varied application contexts.","url_abs":"http://arxiv.org/abs/1809.07499v1","url_pdf":"http://arxiv.org/pdf/1809.07499v1.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":"mason-a-model-agnostic-objectness-framework","repo_url":"https://github.com/JosephKJ/MASON","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}