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Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots.
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang · 2018
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Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Invariant information clustering for unsupervised image classification and segmentation
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
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Language models are few-shot learners
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Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
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Space: Unsupervised object-oriented scene representation via spatial attention and decomposition
Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, and Sungjin Ahn · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Learning object-centric video models by contrasting sets
Sindy Löwe, Klaus Greff, Rico Jonschkowski, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Instance adaptive self-training for unsupervised domain adaptation
Ke Mei, Chuang Zhu, Jiaqi Zou, and Shanghang Zhang · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An empirical study of training self-supervised vision transformers
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Jakob Uszkoreit, Mostafa Dehghani Neil Houlsby, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Taming transformers for high-resolution image synthesis
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Efficient visual pretraining with contrastive detection
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Cycle self-training for domain adaptation
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Unsupervised Layered Image Decomposition into Object Prototypes
Tom Monnier, Elliot Vincent, Jean Ponce, and Mathieu Aubry · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Self-supervised visual representation learning with semantic grouping
Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, and Xiaojuan Qi · 2022
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Promising or elusive? unsupervised object segmentation from real-world single images
Yafei Yang and Bo Yang · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu · 2022
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Localizing objects with self-supervised transformers and no labels
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Unsupervised semantic segmentation by contrasting object mask proposals
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Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning
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Benchmarking unsupervised object representations for video sequences
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Interactive self-training with mean teachers for semi-supervised object detection
Qize Yang, Xihan Wei, Biao Wang, Xian-Sheng Hua, and Lei Zhang · 2021
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Improving semantic segmentation via efficient self-training
Yi Zhu, Zhongyue Zhang, Chongruo Wu, Zhi Zhang, Tong He, Hang Zhang, R Manmatha, Mu Li, and Alexander J Smola · 2021
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Self-supervised object-centric learning for videos
Görkay Aydemir, Weidi Xie, and Fatma Guney · 2023
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Sequential modeling enables scalable learning for large vision models
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Object discovery from motion-guided tokens
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Dorsal: Diffusion for object-centric representations of scenes et al
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Object-centric slot diffusion
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Shepherding slots to objects: Towards stable and robust object-centric learning
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Unsupervised conditional slot attention for object centric learning
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Rotating features for object discovery
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Neural systematic binder
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Exploring the role of the bottleneck in slot-based models through covariance regularization
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Llama 2: Open foundation and fine-tuned chat models, 2023
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Image captioners are scalable vision learners too
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Scalable pre-training of large autoregressive image models
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