Fetching the paper…
Reading the bibliography…
Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly.
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. pp. 722–729. IEEE (2008)
2008
Earlier work this paper cites.
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
2011
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems(NIPS). pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision(ECCV). pp. 740–755 (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. Weston, S.Chopra, A., A.Bordes: Memory networks. International Conference on Learning Representations(ICLR) (2015)
2015
Earlier work this paper cites.
Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in neural information processing systems(NIPS). pp. 2440–2448 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Mansimov, E., Parisotto, E., Ba, J., Salakhutdinov, R.: Generating images from captions with attention. International Conference on Learning Representations(ICLR) (2016)
2016
Earlier work this paper cites.
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. Proceedings of the 33 rd International Conference on Machine Learning(ICML) (2016)
2016
Earlier work this paper cites.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in neural information processing systems(NIPS). pp. 2234–2242 (2016)
2016
Earlier work this paper cites.
Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis (2017)
2017
Earlier work this paper cites.
Cha, M., Gwon, Y., Kung, H.: Adversarial nets with perceptual losses for text-to-image synthesis. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). pp. 1–6. IEEE (2017)
2017
Earlier work this paper cites.
Das, R., Zaheer, M., Reddy, S., Mccallum, A.: Question answering on knowledge bases and text using universal schema and memory networks. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(ACL) p. 358–365 (2017)
2017
Earlier work this paper cites.
Feng, Y., Zhang, S., Zhang, A., Wang, D., Abel, A.: Memory-augmented neural machine translation. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing(EMNLP) p. 1390–1399 (2017)
2017
Earlier work this paper cites.
Hao, D., Yu, S., Chao, W., Guo, Y.: Semantic image synthesis via adversarial learning. In: Proceedings of the IEEE international conference on computer vision(ICCV). pp. 5706–5714 (2017)
2017
Earlier work this paper cites.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR). pp. 1125–1134 (2017)
2017
Earlier work this paper cites.
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier gans. Proceedings of the 34 rd International Conference on Machine Learning(ICML) p. 2642–2651 (2017)
2017
Earlier work this paper cites.
Reed, S., Den Oord, A.V., Kalchbrenner, N., Colmenarejo, S.G., Wang, Z., Chen, Y., Belov, D., De Freitas, N.: Parallel multiscale autoregressive density estimation. Proceedings of the 34 rd International Conference on Machine Learning(ICML) pp. 2912–2921 (2017)
2017
Cited alongside, same era.
Scott Reed, Aaron Van Den Oord, N.K.V.B.M.B.N.D.F.: Generating interpretable images with controllable structure. International Conference on Learning Representations(ICLR) (2017)
2017
Cited alongside, same era.
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision(ICCV). pp. 5907–5915 (2017)
2017
Cited alongside, same era.
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision(ICCV). pp. 2223–2232 (2017)
Lao, Q., Havaei, M., Pesaranghader, A., Dutil, F., Jorio, L.D., Fevens, T.: Dual adversarial inference for text-to-image synthesis. In: Proceedings of the IEEE International Conference on Computer Vision(ICCV). pp. 7567–7576 (2019)
2019
Closest in time.
Li, B., Qi, X., Lukasiewicz, T., Torr, P.: Controllable text-to-image generation. In: Advances in Neural Information Processing Systems(NeurIPS). pp. 2063–2073 (2019)
2019
Closest in time.
Li, W., Zhang, P., Zhang, L., Huang, Q., He, X., Lyu, S., Gao, J.: Object-driven text-to-image synthesis via adversarial training. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR) pp. 12174–12182 (2019)
2019
Closest in time.
Lv, F., Lu, F.: Attention-guided low-light image enhancement. arXiv preprint arXiv:1908.00682 (2019)
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). pp. 6077–6086 (2018)
2018
Cited alongside, same era.
Hong, S., Yang, D., Choi, J., Lee, H.: Inferring semantic layout for hierarchical text-to-image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). pp. 7986–7994 (2018)
2018
Cited alongside, same era.
Ma, C., Shen, C., Dick, A., Den Hengel, A.V.: Visual question answering with memory-augmented networks. Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR) p. 6975–6984 (2018)
2018
Cited alongside, same era.
Maruf, S., Haffari, G.: Document context neural machine translation with memory networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(ACL) p. 1275–1284 (2018)
2018
Cited alongside, same era.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. International Conference on Learning Representations(ICLR) (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
2018
Cited alongside, same era.
Wang, S., Mazumder, S., Liu, B., Zhou, M., Chang, Y.: Target-sensitive memory networks for aspect sentiment classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(ACL) pp. 957–967 (2018)
2018
Cited alongside, same era.
Niu, Y., Gu, L., Lu, F., Lv, F., Wang, Z., Sato, I., Zhang, Z., Xiao, Y., Dai, X., Cheng, T.: Pathological evidence exploration in deep retinal image diagnosis. In: Proceedings of the AAAI conference on artificial intelligence(AAAI). vol. 33, pp. 1093–1101 (2019)
2019
Closest in time.
Pei, W., Zhang, J., Wang, X., Ke, L., Shen, X., Tai, Y.W.: Memory-attended recurrent network for video captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). pp. 8347–8356 (2019)
2019
Closest in time.
Qiao, T., Zhang, J., Xu, D., Tao, D.: Learn, imagine and create: Text-to-image generation from prior knowledge. In: Advances in Neural Information Processing Systems((NeurIPS)). pp. 885–895 (2019)
2019
Closest in time.
Qiao, T., Zhang, J., Xu, D., Tao, D.: Mirrorgan: Learning text-to-image generation by redescription. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR) pp. 4321–4330 (2019)
2019
Closest in time.
Tan, L., Li, Y., Zhang: Semantics-enhanced adversarial nets for text-to-image synthesis. Proceedings of the IEEE international conference on computer vision(ICCV) p. 10501–10510 (2019)
2019
Closest in time.
Yin, G., Liu, B., Sheng, L., Yu, N., Wang, X., Shao, J.: Semantics disentangling for text-to-image generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR) pp. 2327–2336 (2019)
2019
Closest in time.
Yu, H., Cai, M., Liu, Y., Lu, F.: What i see is what you see: Joint attention learning for first and third person video co-analysis. In: Proceedings of the 27th ACM International Conference on Multimedia(ACMMM). pp. 1358–1366 (2019)
2019
Closest in time.
Yuan, M., Peng, Y.: Bridge-gan: Interpretable representation learning for text-to-image synthesis. IEEE Transactions on Circuits and Systems for Video Technology(TCSVT) (2019)
2019
Closest in time.
Yuan, M., Peng, Y.: Ckd: Cross-task knowledge distillation for text-to-image synthesis. IEEE Transactions on Multimedia(TMM) (2019)
2019
Closest in time.
Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. Proceedings of the 36 rd International Conference on Machine Learning(ICML) (2019)
2019
Closest in time.
2019
Closest in time.
Liu, Y., Li, Y., You, S., Lu, F.: Unsupervised learning for intrinsic image decomposition from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). pp. 3248–3257 (2020)
2020
Closest in time.
Liu, Y., Lu, F.: Separate in latent space: Unsupervised single image layer separation. In: Proceedings of the AAAI conference on artificial intelligence(AAAI). pp. 11661–11668 (2020)
2020
Closest in time.