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Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
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Language, character and action: A linguistic approach to the analysis of character in a hemingway short story
HEMINGWAY SHORT STORY Martin Montgomery. 2004 · 2004
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Axiom-based grad-cam: Towards accurate visualization and explanation of cnns
Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, and Biao Li. 2020 · 2008
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Eigen-cam: Class activation map using principal components
Mohammed Bany Muhammad and Mohammed Yeasin. 2020 · 2008
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2014 · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero. 2014 · 2014
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015 · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
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On convergence and stability of gans
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. 2017 · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017 · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al. 2017 · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas. 2017 · 2017
Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. 2018 · 2018
Cited alongside, same era.
Imagine this! scripts to compositions to videos
Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha Kembhavi. 2018 · 2018
Cited alongside, same era.
Towards controllable story generation
Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight. 2018 · 2018
Cited alongside, same era.
Improving generalization and stability of generative adversarial networks
Hoang Thanh-Tung, Truyen Tran, and Svetha Venkatesh. 2018 · 2018
Cited alongside, same era.
Content planning for neural story generation with aristotelian rescoring
Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel, and Nanyun Peng. 2020 · 2020
Later among the works it cites.
Improved-storygan for sequential images visualization
Chunye Li, Liya Kong, and Zhiping Zhou. 2020 · 2020
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Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization
Harish Guruprasad Ramaswamy et al. 2020 · 2020
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Character-preserving coherent story visualization
Yun-Zhu Song, Zhi Rui Tam, Hung-Jen Chen, Huiao-Han Lu, and Hong-Han Shuai. 2020 · 2020
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Score-cam: Score-weighted visual explanations for convolutional neural networks
Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu. 2020 · 2020
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Cogview: Mastering text-to-image generation via transformers
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Seraphina Goldfarb-Tarrant, Haining Feng, and Nanyun Peng. 2019 · 2019
Cited alongside, same era.
Storygan: A sequential conditional gan for story visualization
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. 2019 · 2019
Cited alongside, same era.
Mirrorgan: Learning text-to-image generation by redescription
Tingting Qiao, Jing Zhang, Duanqing Xu, and Dacheng Tao. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Plan-and-write: Towards better automatic storytelling
Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019 · 2019
Cited alongside, same era.
Vaegan: A collaborative filtering framework based on adversarial variational autoencoders
Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019 · 2019
Cited alongside, same era.
Pororogan: An improved story visualization model on pororo-sv dataset
Gangyan Zeng, Zhaohui Li, and Yuan Zhang. 2019 · 2019
Cited alongside, same era.
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. 2021 · 2021
Later among the works it cites.
Integrating visuospatial, linguistic, and commonsense structure into story visualization
Adyasha Maharana and Mohit Bansal. 2021 · 2021
Later among the works it cites.
Improving generation and evaluation of visual stories via semantic consistency
Adyasha Maharana, Darryl Hannan, and Mohit Bansal. 2021 · 2021
Later among the works it cites.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Later among the works it cites.
Videogpt: Video generation using vq-vae and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. 2021 · 2021
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Go back in time: Generating flashbacks in stories with event temporal prompts
Rujun Han, Hong Chen, Yufei Tian, and Nanyun Peng. 2022 · 2022
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