{"extern":"1","_id":"14326","quality_controlled":"1","conference":{"end_date":"2020-12-12","location":"Virtual","name":"NeurIPS: Neural Information Processing Systems","start_date":"2020-12-06"},"date_published":"2020-01-01T00:00:00Z","volume":33,"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","date_updated":"2023-09-13T12:19:19Z","department":[{"_id":"FrLo"}],"oa_version":"Preprint","publication_identifier":{"isbn":["9781713829546"]},"article_processing_charge":"No","intvolume":"        33","citation":{"short":"F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, T. Kipf, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 11525–11538.","apa":"Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., … Kipf, T. (2020). Object-centric learning with slot attention. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 11525–11538). Virtual: Curran Associates.","chicago":"Locatello, Francesco, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. “Object-Centric Learning with Slot Attention.” In <i>Advances in Neural Information Processing Systems</i>, 33:11525–38. Curran Associates, 2020.","ama":"Locatello F, Weissenborn D, Unterthiner T, et al. Object-centric learning with slot attention. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:11525-11538.","ieee":"F. Locatello <i>et al.</i>, “Object-centric learning with slot attention,” in <i>Advances in Neural Information Processing Systems</i>, Virtual, 2020, vol. 33, pp. 11525–11538.","ista":"Locatello F, Weissenborn D, Unterthiner T, Mahendran A, Heigold G, Uszkoreit J, Dosovitskiy A, Kipf T. 2020. Object-centric learning with slot attention. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 33, 11525–11538.","mla":"Locatello, Francesco, et al. “Object-Centric Learning with Slot Attention.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 11525–38."},"title":"Object-centric learning with slot attention","page":"11525-11538","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2006.15055"}],"publication":"Advances in Neural Information Processing Systems","language":[{"iso":"eng"}],"author":[{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"},{"first_name":"Dirk","last_name":"Weissenborn","full_name":"Weissenborn, Dirk"},{"first_name":"Thomas","last_name":"Unterthiner","full_name":"Unterthiner, Thomas"},{"first_name":"Aravindh","last_name":"Mahendran","full_name":"Mahendran, Aravindh"},{"last_name":"Heigold","first_name":"Georg","full_name":"Heigold, Georg"},{"last_name":"Uszkoreit","first_name":"Jakob","full_name":"Uszkoreit, Jakob"},{"first_name":"Alexey","last_name":"Dosovitskiy","full_name":"Dosovitskiy, Alexey"},{"last_name":"Kipf","first_name":"Thomas","full_name":"Kipf, Thomas"}],"year":"2020","external_id":{"arxiv":["2006.15055"]},"oa":1,"abstract":[{"lang":"eng","text":"Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.\r\n\r\n"}],"publisher":"Curran Associates","publication_status":"published","date_created":"2023-09-13T12:03:46Z"}