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Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e.g.
Quantifying the carbon emissions of machine learning
Lacoste, A.; Luccioni, A.; Schmidt, V.; and Dandres, T. 2019 · 1910
Earlier work this paper cites.
IFRNet: Intermediate feature refine network for efficient frame interpolation
Kong, L.; Jiang, B.; Luo, D.; Chu, W.; Huang, X.; Tai, Y.; Wang, C.; and Yang, J. 2022 · 1978
Earlier work this paper cites.
On the optimal presentation duration for subjective video quality assessment
Moss, F. M.; Wang, K.; Zhang, F.; Baddeley, R.; and Bull, D. R. 2015 · 1987
Earlier work this paper cites.
500-11, Methodology for the subjective assessment of the quality of television pictures,”
ITU-R BT, R. 2002 · 2002
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
Earlier work this paper cites.
Model compression
Buciluǎ, C.; Caruana, R.; and Niculescu-Mizil, A. 2006 · 2006
Earlier work this paper cites.
Three-dimensional reconstruction of the digestive wall in capsule endoscopy videos using elastic video interpolation
Karargyris, A.; and Bourbakis, N. 2010 · 2010
Earlier work this paper cites.
A database and evaluation methodology for optical flow
Baker, S.; Scharstein, D.; Lewis, J.; Roth, S.; Black, M. J.; and Szeliski, R. 2011 · 2011
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Soomro, K.; Zamir, A. R.; and Shah, M. 2012 · 2012
Earlier work this paper cites.
Learning separable filters
Rigamonti, R.; Sironi, A.; Lepetit, V.; and Fua, P. 2013 · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
Earlier work this paper cites.
Deepstereo: Learning to predict new views from the world’s imagery
Flynn, J.; Neulander, I.; Philbin, J.; and Snavely, N. 2016 · 2016
Earlier work this paper cites.
A benchmark dataset and evaluation methodology for video object segmentation
Perazzi, F.; Pont-Tuset, J.; McWilliams, B.; Van Gool, L.; Gross, M.; and Sorkine-Hornung, A. 2016 · 2016
Earlier work this paper cites.
Deformable convolutional networks
Dai, J.; Qi, H.; Xiong, Y.; Li, Y.; Zhang, G.; Hu, H.; and Wei, Y. 2017 · 2017
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017 · 2017
Earlier work this paper cites.
Video frame synthesis using deep voxel flow
Liu, Z.; Yeh, R. A.; Tang, X.; Liu, Y.; and Agarwala, A. 2017 · 2017
Earlier work this paper cites.
Video frame interpolation via adaptive convolution
Niklaus, S.; Mai, L.; and Liu, F. 2017 · 2017
Earlier work this paper cites.
Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Super slomo: High quality estimation of multiple intermediate frames for video interpolation
Jiang, H.; Sun, D.; Jampani, V.; Yang, M.-H.; Learned-Miller, E.; and Kautz, J. 2018 · 2018
Cited alongside, same era.
A study of high frame rate video formats
Mackin, A.; Zhang, F.; and Bull, D. R. 2018 · 2018
Cited alongside, same era.
Context-aware synthesis for video frame interpolation
Niklaus, S.; and Liu, F. 2018 · 2018
Cited alongside, same era.
Video compression through image interpolation
Wu, C.-Y.; Singhal, N.; and Krahenbuhl, P. 2018 · 2018
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Esser, P.; Rombach, R.; and Ommer, B. 2021 · 2021
Later among the works it cites.
BVI-DVC: A Training Database for Deep Video Compression
Ma, D.; Zhang, F.; and Bull, D. 2021 · 2021
Later among the works it cites.
Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
Park, J.; Lee, C.; and Kim, C.-S. 2021 · 2021
Later among the works it cites.
XVFI: eXtreme Video Frame Interpolation
Sim, H.; Oh, J.; and Kim, M. 2021 · 2021
Later among the works it cites.
Deep animation video interpolation in the wild
Siyao, L.; Zhao, S.; Yu, W.; Sun, W.; Metaxas, D.; Loy, C. C.; and Liu, Z. 2021 · 2021
Later among the works it cites.
Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2021 · 2021
Later among the works it cites.
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
Cited alongside, same era.
Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
Cited alongside, same era.
Video enhancement with task-oriented flow
Xue, T.; Chen, B.; Wu, J.; Wei, D.; and Freeman, W. T. 2019 · 2019
Cited alongside, same era.
Video frame interpolation via deformable separable convolution
Cheng, X.; and Chen, Z. 2020 · 2020
Cited alongside, same era.
Channel attention is all you need for video frame interpolation
Choi, M.; Kim, H.; Han, B.; Xu, N.; and Lee, K. M. 2020 · 2020
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2020 · 2020
Cited alongside, same era.
NTIRE 2021 challenge on quality enhancement of compressed video: Dataset and study
Yang, R. 2021 · 2021
Later among the works it cites.
Multiple video frame interpolation via enhanced deformable separable convolution
Cheng, X.; and Chen, Z. 2022 · 2022
Later among the works it cites.
Cascaded Diffusion Models for High Fidelity Image Generation
Ho, J.; Saharia, C.; Chan, W.; Fleet, D. J.; Norouzi, M.; and Salimans, T. 2022 · 2022
Later among the works it cites.
Elucidating the Design Space of Diffusion-Based Generative Models
Karras, T.; Aittala, M.; Aila, T.; and Laine, S. 2022 · 2022
Later among the works it cites.
Srdiff: Single image super-resolution with diffusion probabilistic models
Li, H.; Yang, Y.; Chang, M.; Chen, S.; Feng, H.; Xu, Z.; Li, Q.; and Chen, Y. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Later among the works it cites.
Progressive Distillation for Fast Sampling of Diffusion Models
Salimans, T.; and Ho, J. 2022 · 2022
Later among the works it cites.
Maxvit: Multi-axis vision transformer
Tu, Z.; Talebi, H.; Zhang, H.; Yang, F.; Milanfar, P.; Bovik, A.; and Li, Y. 2022 · 2022
Later among the works it cites.
MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
Voleti, V.; Jolicoeur-Martineau, A.; and Pal, C. 2022 · 2022
Later among the works it cites.
Diffusion models: A comprehensive survey of methods and applications
Yang, L.; Zhang, Z.; Song, Y.; Hong, S.; Xu, R.; Zhao, Y.; Shao, Y.; Zhang, W.; Cui, B.; and Yang, M.-H. 2022 · 2022
Later among the works it cites.
Perceptual Learned Video Compression with Recurrent Conditional GAN
Yang, R.; Timofte, R.; and Van Gool, L. 2022 · 2022
Later among the works it cites.
NTIRE 2023 video colorization challenge
Kang, X.; Lin, X.; Zhang, K.; Hui, Z.; Xiang, W.; He, J.-Y.; Li, X.; Ren, P.; Xie, X.; Timofte, R.; et al. 2023 · 2023
Closest in time.
ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation
Morris, C.; Danier, D.; Zhang, F.; Anantrasirichai, N.; and Bull, D. R. 2023 · 2023
Closest in time.
Perceptual Video Quality Assessment: The Journey Continues!
Saha, A.; Pentapati, S. K.; Shang, Z.; Pahwa, R.; Chen, B.; Gedik, H. E.; Mishra, S.; and Bovik, A. C. 2023 · 2023
Closest in time.
FLAVR: Flow-agnostic video representations for fast frame interpolation
Kalluri, T.; Pathak, D.; Chandraker, M.; and Tran, D. 2023 · 2082
Closest in time.