A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation
暂无分享,去创建一个
Ronggang Wang | Yang Zhao | Haoxian Zhang | Ronggang Wang | Yangshen Zhao | Haoxian Zhang | Haoxian Zhang
[1] Feng Liu,et al. Context-Aware Synthesis for Video Frame Interpolation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[3] Thomas Brox,et al. High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.
[4] Mubarak Shah,et al. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.
[5] Stefan Roth,et al. Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Max Grosse,et al. Phase-based frame interpolation for video , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Cordelia Schmid,et al. EpicFlow: Edge-preserving interpolation of correspondences for optical flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[9] Jiajun Wu,et al. Video Enhancement with Task-Oriented Flow , 2018, International Journal of Computer Vision.
[10] Bingbing Ni,et al. Unsupervised Deep Learning for Optical Flow Estimation , 2017, AAAI.
[11] Xiaoou Tang,et al. Video Frame Synthesis Using Deep Voxel Flow , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] Michael R. Lyu,et al. SelFlow: Self-Supervised Learning of Optical Flow , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] John Flynn,et al. Deep Stereo: Learning to Predict New Views from the World's Imagery , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Hongdong Li,et al. Learning Image Matching by Simply Watching Video , 2016, ECCV.
[15] Jan Kautz,et al. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[16] Yung-Yu Chuang,et al. Deep Video Frame Interpolation Using Cyclic Frame Generation , 2019, AAAI.
[17] Luc Van Gool,et al. A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Zhiyong Gao,et al. MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Luc Van Gool,et al. Fast Optical Flow Using Dense Inverse Search , 2016, ECCV.
[20] Thomas Brox,et al. FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] Xiaoyun Zhang,et al. Depth-Aware Video Frame Interpolation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Richard Szeliski,et al. A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[24] Jan Kautz,et al. Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[25] Yasuyuki Matsushita,et al. Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[26] Alejandro Acosta,et al. Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks , 2017, ArXiv.
[27] Ronggang Wang,et al. Multi-Frame Pyramid Refinement Network for Video Frame Interpolation , 2019, IEEE Access.
[28] Cordelia Schmid,et al. DeepFlow: Large Displacement Optical Flow with Deep Matching , 2013, 2013 IEEE International Conference on Computer Vision.
[29] Feng Liu,et al. Video Frame Interpolation via Adaptive Convolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[31] Michael J. Black,et al. Optical Flow Estimation Using a Spatial Pyramid Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Oisin Mac Aodha,et al. Unsupervised Monocular Depth Estimation with Left-Right Consistency , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[34] Yi Yang,et al. Occlusion Aware Unsupervised Learning of Optical Flow , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Thomas Brox,et al. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Bernard Ghanem,et al. ActivityNet: A large-scale video benchmark for human activity understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Stefan Roth,et al. UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss , 2017, AAAI.
[38] Feng Liu,et al. Video Frame Interpolation via Adaptive Separable Convolution , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[39] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[40] F. Bossen,et al. Common test conditions and software reference configurations , 2010 .