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Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as prediction and reasoning.
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On the binding problem in artificial neural networks
K. Greff, S. Van Steenkiste, and J. Schmidhuber · 2020
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Z. Lin, Y.-F. Wu, S. Peri, B. Fu, J. Jiang, and S. Ahn · 2020
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Object-centric learning with slot attention
F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, and T. Kipf · 2020
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L. Pantelis, P. Vasilis, and K. Sotiris · 2020
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A. Chakravarthy, T. Nguyen, A. Goyal, Y. Bengio, and M. C. Mozer · 2023
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FAENet: Frame averaging equivariant GNN for materials modeling
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S. Ferraro, P. Mazzaglia, T. Verbelen, and B. Dhoedt · 2023
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B. Jia, Y. Liu, and S. Huang · 2023
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Spot: Self-training with patch-order permutation for object-centric learning with autoregressive transformers, 2023
I. Kakogeorgiou, S. Gidaris, K. Karantzalos, and N. Komodakis · 2023
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A. Nakano, M. Suzuki, and Y. Matsuo · 2023
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Bridging the gap to real-world object-centric learning
M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J. Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, et al · 2023
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An investigation into pre-training object-centric representations for reinforcement learning
J. Yoon, Y.-F. Wu, H. Bae, and S. Ahn · 2023
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Object-centric learning for real-world videos by predicting temporal feature similarities
A. Zadaianchuk, M. Seitzer, and G. Martius · 2023
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Cycle consistency driven object discovery
A. Didolkar, A. Goyal, and Y. Bengio · 2024
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Object centric architectures enable efficient causal representation learning
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Neural language of thought models
Y.-F. Wu, M. Lee, and S. Ahn · 2024
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