[1] Titas Anciukevicius, Christoph H Lampert, and Paul Henderson. Object-centric image generation with factored depths, locations, and appearances. arXiv preprint arXiv:2004.00642, 2020.
[2] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.
[3] Federico Baldassarre and Hossein Azizpour. Towards self-supervised learning of global and object-centric representations. ICLR 2022 Workshop on the Elements of Reasoning: Objects, Structure, and Causality, 2022.
[4] Christopher P Burgess, Loic Matthew, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner. MONet: Unsupervised scene decomposition and representation. arXiv preprint arXiv:1901.11390, 2019.
[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In International Conference on Computer Vision (ICCV), pp. 9650-9660, 2021.
[6] Chang Chen, Fei Deng, and Sungjin Ahn. Roots: Object-centric representation and rendering of 3d scenes. The Journal of Machine Learning Research, 22(1):11770-11805, 2021.
[7] Eric Crawford and Joelle Pineau. Spatially invariant unsupervised object detection with convolutional neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp. 3412-3420, 2019.
[8] Stanislas Dehaene. How we learn: Why brains learn better than any machine... for now. Penguin, 2021.
[9] Fei Deng, Zhuo Zhi, Donghun Lee, and Sungjin Ahn. Generative scene graph networks. In International Conference on Learning Representations (ICLR), 2020.
[10] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE, 2009.
[11] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations (ICLR), 2021.
[12] Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, and Igor Mordatch. Unsupervised learning of compositional energy concepts. Advances in Neural Information Processing Systems (NeurIPS), 34:15608-15620, 2021.
[13] Yilun Du, Kevin Smith, Tomer Ulman, Joshua Tenenbaum, and Jiajun Wu. Unsupervised discovery of 3d physical objects from video. International Conference on Learning Representations (ICLR), 2021.
[14] Gamaleldin Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C Mozer, and Thomas Kipf. Savi++: Towards end-to-end object-centric learning from real-world videos. Advances in Neural Information Processing Systems (NeurIPS), 35:28940-28954, 2022.
[15] Andreas K Engel, Pieter R Roelfsema, Pascal Fries, Michael Brecht, and Wolf Singer. Role of the temporal domain for response selection and perceptual binding. *Cerebral Cortex* (New York, NY: 1991), 7(6):571-582, 1997.
[16] Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner. GENESIS: Generative scene inference and sampling with object-centric latent representations. International Conference on Learning Representations (ICLR), 2020.