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Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning.
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Multi-object representation learning with iterative variational inference
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Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Scalor: Generative world models with scalable object representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 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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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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Latent compositional representations improve systematic generalization in grounded question answering
Ben Bogin, Sanjay Subramanian, Matt Gardner, and Jonathan Berant · 2021
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How modular should neural module networks be for systematic generalization?
Vanessa D’Amario, Tomotake Sasaki, and Xavier Boix · 2021
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Genesis-v2: Inferring unordered object representations without iterative refinement
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
Photorealistic text-to-image diffusion models with deep language understanding
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Object scene representation transformer
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Bridging the gap to real-world object-centric learning
Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, et al · 2022
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Self-supervised visual representation learning with semantic grouping
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Tackling the generative learning trilemma with denoising diffusion gans
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Laurynas Karazija, Iro Laina, and Christian Rupprecht · 2021
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Compositional networks enable systematic generalization for grounded language understanding
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Dynamic inference with neural interpreters
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Decomposing 3d scenes into objects via unsupervised volume segmentation
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Object-centric compositional imagination for visual abstract reasoning
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Object representations as fixed points: Training iterative refinement algorithms with implicit differentiation
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Generalization and robustness implications in object-centric learning
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Solving 3d inverse problems using pre-trained 2d diffusion models
Hyungjin Chung, Dohoon Ryu, Michael T McCann, Marc L Klasky, and Jong Chul Ye · 2023
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Object-centric slot diffusion
Jindong Jiang, Fei Deng, Gautam Singh, and Sungjin Ahn · 2023
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Shepherding slots to objects: Towards stable and robust object-centric learning
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Multi-concept customization of text-to-image diffusion
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Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, and Alan L Yuille · 2023
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More control for free! image synthesis with semantic diffusion guidance
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Dinov2: Learning robust visual features without supervision
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Inversion-based style transfer with diffusion models
Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu · 2023
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Slotdiffusion: Object-centric generative modeling with diffusion models
Ziyi Wu, Jingyu Hu, Wuyue Lu, Igor Gilitschenski, and Animesh Garg · 2024
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