{"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/stampnet-unsupervised-multi-class-object","title":"StampNet: unsupervised multi-class object discovery","arxiv_id":"1902.02693","date":"2019-02-07","proceeding":null,"authors":["Joost Visser","Alessandro Corbetta","Vlado Menkovski","Federico Toschi"],"abstract":"Unsupervised object discovery in images involves uncovering recurring\npatterns that define objects and discriminates them against the background.\nThis is more challenging than image clustering as the size and the location of\nthe objects are not known: this adds additional degrees of freedom and\nincreases the problem complexity. In this work, we propose StampNet, a novel\nautoencoding neural network that localizes shapes (objects) over a simple\nbackground in images and categorizes them simultaneously. StampNet consists of\na discrete latent space that is used to categorize objects and to determine the\nlocation of the objects. The object categories are formed during the training,\nresulting in the discovery of a fixed set of objects. We present a set of\nexperiments that demonstrate that StampNet is able to localize and cluster\nmultiple overlapping shapes with varying complexity including the digits from\nthe MNIST dataset. We also present an application of StampNet in the\nlocalization of pedestrians in overhead depth-maps.","url_abs":"http://arxiv.org/abs/1902.02693v1","url_pdf":"http://arxiv.org/pdf/1902.02693v1.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":"stampnet-unsupervised-multi-class-object","repo_url":"https://github.com/crowdflowTUe/stampnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-discovery","task_name":"Object Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.02693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.02693"}},"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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