[1] Gallego G., Rebecq H. and Scaramuzza D., A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and optical Flow Estimation, IEEE Conference on Computer Vision and Pattern Recognition, 2018.
[2] Enrico C., Gemma T., Christopher A.E., Sophie S., Federico C., Luca L., Kynan E. and Tobi D., DHP19: Dynamic Vision Sensor 3D Human Pose Dataset, IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019.
[3] Cedric S., Nick B. and Robert M., Continuous-Time Intensity Estimation Using Event Cameras, Asian Conference on Computer Vision, 308 (2018).
[4] Kamyar N., Eric Ng, Tony J., Faisal Z. Qureshi and Mehran E., EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super- Resolution via End-to-End Adversarial Learning, IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[5] Jing-yuan Li, Ning Wang, Le-fei Zhang, Bo Du and Da-cheng Tao, Recurrent Feature Reasoning for Image Inpainting, IEEE Conference on Computer Vision and Pattern Recognition, 7760 (2020).
[6] Yi W., Ying-cong C., Xin T. and Jia-ya J., VCNet: A Robust Approach to Blind Image Inpainting, IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[7] Ya-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee and Winston Hsu, Free-Form Image Inpainting with Gated Convolution, International Conference on Computer Vision, 4470 (2019).
[8] Chen Gao, Ayush Saraf, Jia-Bin Huang and Johannes Kopf, Flow-edge Guided Video Completion, European Conference on Computer Vision, 2020.
[9] Alex Zihao Zhu, Dinesh T., Tolga O., Bernd P., Vijay K. and Kostas D., IEEE Robotics and Automation Letters 3, 2032 (2018).
[10] Patrick L., Christoph P. and Tobi D., J Solid-State Circuits 43, 566 (2008).
[11] Pini S., Borghi G. and Vezzani Ro., International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4, 37 (2020).
[12] Alex Zihao Zhu, Liang-zhe Yuan, Kenneth C. and Kostas D., EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras, Robotics: Science and Systems, 2018.
[13] Kai-ming He, Xiang-yu Zhang, Shao-qing Ren and Jian Sun, Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition, 770 (2016).
[14] Dmitry U, Andera V and Victor L, Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis, IEEE Conference on Computer Vision and Pattern Recognition, 4105 (2017).
[15] Phillip I., Jun-Yan Zhu, Ting-hui Zhou and Alexei A. E., Image-to-Image Translation with Conditional Adversarial Networks, IEEE Conference on Computer Vision and Pattern Recognition, 5967 (2017).
[16] Miyato T., Kataoka T., Koyama M. and Yoshida Y., Spectral Normalization for Generative Adversarial Networks, International Conference on Learning Representations, 2018.
[17] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville and Yoshua Bengio, Generative Adversarial Nets, Annual Conference on Neural Information Processing Systems, 2672 (2014).
[18] Johnson J, Alahi A and Fei-Fei L., Perceptual Losses for Real-Time Style Transfer and Super-Resolution, European Conference on Computer Vision, 694 (2016).
[19] Leon A.G., Alexander S.E and Matthias B., Image Style Transfer Using Convolutional Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition, 2414 (2016).
[20] Kingma DP and Ba J., Adam: A Method for Stochastic Optimization, International Conference on Learning Representations, 2015.
[21] Newell A., Yang K. and Deng J., Stacked Hourglass Networks for Human Pose Estimation, European Conference on Computer Vision, 483 (2016).