{"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/cuvler-enhanced-unsupervised-object","title":"CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers","arxiv_id":"2403.07700","date":"2024-03-12","proceeding":"CVPR 2024 1","authors":["Shahaf Arica","Or Rubin","Sapir Gershov","Shlomi Laufer"],"abstract":"In this paper, we introduce VoteCut, an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. 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