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Despite the impressive advances in text-to-image models, they often struggle to effectively compose complex scenes with multiple objects, displaying various attributes and relationships.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in ICVGIP , 2008
2008
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011
2011
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in NeurIPS , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” in ICML , 2016
2016
Earlier work this paper cites.
S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee, “Learning what and where to draw,” in NeurIPS , 2016
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in NeurIPS , 2016
2016
Earlier work this paper cites.
H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas, “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in ICCV , 2017
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in NeurIPS , 2017
2017
Earlier work this paper cites.
G. Neuhold, T. Ollmann, S. Rota Bulo, and P. Kontschieder, “The mapillary vistas dataset for semantic understanding of street scenes,” in ICCV , 2017
2017
Earlier work this paper cites.
T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He, “Attngan: Fine-grained text to image generation with attentional generative adversarial networks,” in CVPR , 2018
2018
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in ICLR , 2018
2018
Earlier work this paper cites.
M. Zhu, P. Pan, W. Chen, and Y. Yang, “Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis,” in CVPR , 2019
2019
Earlier work this paper cites.
S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun, “Objects365: A large-scale, high-quality dataset for object detection,” in ICCV , 2019
2019
Earlier work this paper cites.
D. Teney, E. Abbasnejad, K. Kafle, R. Shrestha, C. Kanan, and A. Van Den Hengel, “On the value of out-of-distribution testing: An example of goodhart’s law,” Advances in neural information processing systems , vol. 33, pp. 407–417, 2020
2020
Earlier work this paper cites.
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov et al. , “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” in IJCV , 2020
2020
Earlier work this paper cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” in NeurIPS , 2021
2021
Earlier work this paper cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” ICLR , 2021
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in ICML , 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
H. Zhang, J. Y. Koh, J. Baldridge, H. Lee, and Y. Yang, “Cross-modal contrastive learning for text-to-image generation,” in CVPR , 2021
2021
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in ICML , 2021
2021
Earlier work this paper cites.
D. H. Park, S. Azadi, X. Liu, T. Darrell, and A. Rohrbach, “Benchmark for compositional text-to-image synthesis,” in NeurIPS , 2021
2021
Earlier work this paper cites.
R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” in ICCV , 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022
2022
Earlier work this paper cites.
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans, “Cascaded diffusion models for high fidelity image generation,” The Journal of Machine Learning Research , 2022
2022
Earlier work this paper cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” in NeurIPS , 2022
2022
Earlier work this paper cites.
N. Liu, S. Li, Y. Du, A. Torralba, and J. B. Tenenbaum, “Compositional visual generation with composable diffusion models,” in ECCV , 2022
2022
Earlier work this paper cites.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in ICML , 2022
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” in ICML , 2022
2022
Cited alongside, same era.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” in NeurIPS , 2022
2022
Cited alongside, same era.
O. Gafni, A. Polyak, O. Ashual, S. Sheynin, D. Parikh, and Y. Taigman, “Make-a-scene: Scene-based text-to-image generation with human priors,” in ECCV , 2022
2022
Cited alongside, same era.
2023
Closest in time.
J. Cho, A. Zala, and M. Bansal, “Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models,” in ICCV , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
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Z. Li, M. R. Min, K. Li, and C. Xu, “Stylet2i: Toward compositional and high-fidelity text-to-image synthesis,” in CVPR , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” in NeurIPS , 2022
2022
Cited alongside, same era.
X. Zhou, V. Koltun, and P. Krähenbühl, “Simple multi-dataset detection,” in CVPR , 2022
2022
Cited alongside, same era.
L. H. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J.-N. Hwang et al. , “Grounded language-image pre-training,” in CVPR , 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
W. Feng, X. He, T.-J. Fu, V. Jampani, A. Akula, P. Narayana, S. Basu, X. E. Wang, and W. Y. Wang, “Training-free structured diffusion guidance for compositional text-to-image synthesis,” in ICLR , 2023
2023
Cited alongside, same era.
H. Chefer, Y. Alaluf, Y. Vinker, L. Wolf, and D. Cohen-Or, “Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models,” in ACM Trans. Graph. , 2023
2023
Cited alongside, same era.
2023
Closest in time.
J. Xu, X. Liu, Y. Wu, Y. Tong, Q. Li, M. Ding, J. Tang, and Y. Dong, “Imagereward: Learning and evaluating human preferences for text-to-image generation,” 2023
2023
Closest in time.
2023
Closest in time.
X. Wu, K. Sun, F. Zhu, R. Zhao, and H. Li, “Better aligning text-to-image models with human preference,” 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
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OpenAI, https://openai.com/blog/chatgpt/ , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
B. F. Labs, https://github.com/black-forest-labs/flux , 2024
2024
Closest in time.
P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, D. Podell, T. Dockhorn, Z. English, K. Lacey, A. Goodwin, Y. Marek, and R. Rombach, “Scaling rectified flow transformers for high-resolution image synthesis,” 2024
2024
Closest in time.
J. Chen, J. Yu, C. Ge, L. Yao, E. Xie, Y. Wu, Z. Wang, J. Kwok, P. Luo, H. Lu et al. , “Pixart- a l p h a alpha : Fast training of diffusion transformer for photorealistic text-to-image synthesis,” in ICLR , 2024
2024
Closest in time.
K. Huang, K. Sun, E. Xie, Z. Li, and X. Liu, “T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation,” NeurIPS , 2024
2024
Closest in time.
2024
Closest in time.
M. Chen, I. Laina, and A. Vedaldi, “Training-free layout control with cross-attention guidance,” in WACV , 2024
2024
Closest in time.
R. Rassin, E. Hirsch, D. Glickman, S. Ravfogel, Y. Goldberg, and G. Chechik, “Linguistic binding in diffusion models: Enhancing attribute correspondence through attention map alignment,” NeurIPS , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
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2024
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Y. Kirstain, A. Polyak, U. Singer, S. Matiana, J. Penna, and O. Levy, “Pick-a-pic: An open dataset of user preferences for text-to-image generation,” NeurIPS , 2024
2024
Closest in time.
T. Lee, M. Yasunaga, C. Meng, Y. Mai, J. S. Park, A. Gupta, Y. Zhang, D. Narayanan, H. Teufel, M. Bellagente et al. , “Holistic evaluation of text-to-image models,” NeurIPS , 2024
2024
Closest in time.