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Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Image retrieval using scene graphs
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L. Li, D. A. Shamma, M. S. Bernstein, and F. Li · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Learning what and where to draw
S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee · 2016
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Joint embeddings of scene graphs and images
E. Belilovsky, M. Blaschko, J. R. Kiros, R. Urtasun, and R. Zemel · 2017
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Photographic image synthesis with cascaded refinement networks
Q. Chen and V. Koltun · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Pixels to graphs by associative embedding
A. Newell and J. Deng · 2017
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Exploiting Building Blocks of Data to Efficiently Create Training Sets
P. Varma, B. He, and C. Ré” · 2017
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Inferring generative model structure with static analysis
P. Varma, B. D. He, P. Bajaj, N. Khandwala, I. Banerjee, D. Rubin, and C. Ré · 2017
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Relational inductive biases, deep learning, and graph networks, 2018
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song, A. Ballard, J. Gilmer, G. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu · 2018
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Large scale gan training for high fidelity natural image synthesis, 2018
A. Brock, J. Donahue, and K. Simonyan · 2018
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Coco-stuff: Thing and stuff classes in context
H. Caesar, J. Uijlings, and V. Ferrari · 2018
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Mapping images to scene graphs with permutation-invariant structured prediction
R. Herzig, M. Raboh, G. Chechik, J. Berant, and A. Globerson · 2018
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Inferring semantic layout for hierarchical text-to-image synthesis
S. Hong, D. Yang, J. Choi, and H. Lee · 2018
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Scene graph generation by iterative message passing
D. Xu, Y. Zhu, C. Choy, and L. Fei-Fei · 2017
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Neural motifs: Scene graph parsing with global context
R. Zellers, M. Yatskar, S. Thomson, and Y. Choi · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
H. Zhang, T. Xu, and H. Li · 2017
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Image generation from scene graphs
J. Johnson, A. Gupta, and L. Fei-Fei · 2018
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Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
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Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
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Synthetic depth-of-field with a single-camera mobile phone
N. Wadhwa, R. Garg, D. E. Jacobs, B. E. Feldman, N. Kanazawa, R. Carroll, Y. Movshovitz-Attias, J. T. Barron, Y. Pritch, and M. Levoy · 2018
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