{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:01:52Z","timestamp":1762351312589,"version":"3.37.3"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,9,1]]},"DOI":"10.1109\/tpami.2021.3083839","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T23:26:51Z","timestamp":1622071611000},"page":"5016-5025","source":"Crossref","is-referenced-by-count":9,"title":["IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9412-7165","authenticated-orcid":false,"given":"Ronan","family":"Riochet","sequence":"first","affiliation":[{"name":"Ecole Normale Supérieure, INRIA, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario Ynocente","family":"Castro","sequence":"additional","affiliation":[{"name":"Ecole Normale Supérieure, INRIA, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7586-7133","authenticated-orcid":false,"given":"Mathieu","family":"Bernard","sequence":"additional","affiliation":[{"name":"Ecole Normale Supérieure, INRIA, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Lerer","sequence":"additional","affiliation":[{"name":"Facebook AI Research, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob","family":"Fergus","sequence":"additional","affiliation":[{"name":"Facebook AI Research, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5120-7165","authenticated-orcid":false,"given":"Veronique","family":"Izard","sequence":"additional","affiliation":[{"name":"CNRS, Université Paris Descartes, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel","family":"Dupoux","sequence":"additional","affiliation":[{"name":"CoML, ENS\/CNRS\/EHESS\/INRIA\/PSL Research University, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","first-page":"1990","article-title":"Learning to segment object candidates,","author":"Pinheiro","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8624.2006.00975.x"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0285(83)90017-8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1111\/j.2044-835X.1995.tb00669.x"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.actpsy.2006.05.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/edp.2430010203"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/0885-2014(90)90011-H"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1006\/cogp.1996.0005"},{"key":"ref12","first-page":"4502","article-title":"Interaction networks for learning about objects, relations and physics,","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Battaglia"},{"article-title":"A compositional object-based approach to learning physical dynamics,","year":"2016","author":"Chang","key":"ref13"},{"key":"ref14","first-page":"91","article-title":"Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks,","author":"Xue","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref15","first-page":"3604","article-title":"A disentangled recognition and nonlinear dynamics model for unsupervised learning,","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fraccaro"},{"article-title":"Entity abstraction in visual model-based reinforcement learning,","year":"2020","author":"Veerapaneni","key":"ref16"},{"article-title":"Shapenet: An information-rich 3D model repository,","year":"2015","author":"Chang","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2516982"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref21","first-page":"64","article-title":"Unsupervised learning for physical interaction through video prediction,","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Finn"},{"key":"ref22","first-page":"2863","article-title":"Action-conditional video prediction using deep networks in Atari games,","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst","author":"Oh"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_2"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.387"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0277(85)90008-3"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00115"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-0277(99)00035-9"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.157"},{"key":"ref29","first-page":"33","article-title":"Core cognition and beyond,","volume-title":"Early Childhood Development and Later Outcome","author":"Baillargeon","year":"2012"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1163\/156856888X00122"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1126\/science.7350666"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks,","year":"2015","author":"Radford","key":"ref33"},{"article-title":"Learning visual predictive models of physics for playing billiards,","volume-title":"Proc. Int. Conf. Learn. Representation","author":"Fragkiadaki","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"article-title":"Conditional generative adversarial nets,","year":"2014","author":"Mirza","key":"ref36"},{"article-title":"Semi-supervised learning with context-conditional generative adversarial networks,","year":"2016","author":"Denton","key":"ref37"},{"article-title":"Adam: A method for stochastic optimization,","year":"2014","author":"Kingma","key":"ref38"},{"key":"ref39","first-page":"8983","article-title":"Modeling expectation violation in intuitive physics with coarse probabilistic object representations,","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Smith"},{"article-title":"Occlusion resistant learning of intuitive physics from videos,","year":"2020","author":"Riochet","key":"ref40"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1306572110"},{"key":"ref42","first-page":"430","article-title":"Learning physical intuition of block towers by example,","volume-title":"in Proc. Int. Conf. Mach. Learn.","author":"Lerer"},{"article-title":"A comparative evaluation of approximate probabilistic simulation and deep neural networks as accounts of human physical scene understanding,","volume-title":"Proc. Annu. Meeting Cogn. Sci. Soc. Recognization Representing Events CogSci","author":"Zhang","key":"ref43"},{"article-title":"To fall or not to fall: A visual approach to physical stability prediction,","year":"2016","author":"Li","key":"ref44"},{"article-title":"Generalizable features from unsupervised learning,","volume-title":"Proc. ICLR Workshop Submission","author":"Mirza","key":"ref45"},{"article-title":"Visual stability prediction and its application to manipulation,","year":"2016","author":"Li","key":"ref46"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.39"},{"article-title":"Deep multi-scale video prediction beyond mean square error,","year":"2015","author":"Mathieu","key":"ref48"},{"key":"ref49","first-page":"4502","article-title":"Interaction networks for learning about objects, relations and physics,","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Battaglia"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/springerreference_63882"},{"article-title":"Learning a physical long-term predictor,","year":"2017","author":"Ehrhardt","key":"ref51"},{"key":"ref52","first-page":"269","article-title":"","volume-title":"Proc. Eur. Conf. Comput. Vis","author":"Mottaghi"},{"article-title":"PHYRE: A new benchmark for physical reasoning,","year":"2019","author":"Bakhtin","key":"ref53"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1912341117"},{"article-title":"Probing physics knowledge using tools from developmental psychology,","year":"2018","author":"Piloto","key":"ref55"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9893110\/09442261.pdf?arnumber=9442261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:50:46Z","timestamp":1704844246000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9442261\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,1]]},"references-count":55,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3083839","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2022,9,1]]}}}