{"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-object-permanence-from-video","title":"Learning Object Permanence from Video","arxiv_id":"2003.10469","date":"2020-03-23","proceeding":"ECCV 2020 8","authors":["Aviv Shamsian","Ofri Kleinfeld","Amir Globerson","Gal Chechik"],"abstract":"Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanence is critical for building a model of the world, since objects in natural visual scenes dynamically occlude and contain each-other. Intensive studies in developmental psychology suggest that object permanence is a challenging task that is learned through extensive experience. Here we introduce the setup of learning Object Permanence from data. We explain why this learning problem should be dissected into four components, where objects are (1) visible, (2) occluded, (3) contained by another object and (4) carried by a containing object. The fourth subtask, where a target object is carried by a containing object, is particularly challenging because it requires a system to reason about a moving location of an invisible object. We then present a unified deep architecture that learns to predict object location under these four scenarios. We evaluate the architecture and system on a new dataset based on CATER, and find that it outperforms previous localization methods and various baselines.","url_abs":"https://arxiv.org/abs/2003.10469v4","url_pdf":"https://arxiv.org/pdf/2003.10469v4.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-object-permanence-from-video","repo_url":"https://github.com/ofrikleinfeld/ObjectPermanence","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"video-object-tracking","task_name":"Video Object Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-object-tracking-on-cater","task":"Video Object Tracking","dataset":"CATER","model":"OPNet","rank_in_archive_order":3,"of":7,"metrics":{"L1":"0.54","Top 1 Accuracy":"74.8"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2003.10469","atlas_url":"https://app.syntology.ai/?focus=2003.10469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10469"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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