The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Video Frame Interpolation via Adaptive Separable Convolution
Open accessAbstract
Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D kernels require significantly fewer parameters to be estimated. Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously. Since our method is able to estimate kernels and synthesizes the whole video frame at once, it allows for the incorporation of perceptual loss to train the neural network to produce visually pleasing frames. This deep neural network is trained end-to-end using widely available video data without any human annotation. Both qualitative and quantitative experiments show that our method provides a practical solution to high-quality video frame interpolation.
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Cites
Adam: A Method for Stochastic Optimization
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Image Super-Resolution Using Deep Convolutional Networks
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Flownet: Learning optical flow with convolutional networks
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“Common frame interpolation approaches estimate dense motion, typically optical flow, between two input frames and then interpolate one or more intermediate frames guided by the motion [3, 53, 60].”
BackgroundMethodInfluentialUnsupervised Learning of Video Representations using LSTMs
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Cited by
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273
“The AdaMax algorithm [98] is a variant of Adam that makes the boundary range of the learning rate simpler, and it has been used to train CNN models [101], [102].”
MethodSelf-citationDeep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
Dynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
Image and Video Compression With Neural Networks: A Review
Ma, S., Zhang, X., Jia, C., Zhao, Z., Wang, S., & Wang, S. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2910119 · Cited by 459
Metadata
- Authors
- Niklaus, S., Mai, L., & Liu, F.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 261-270
- OpenAlex
- W2964251418
- Cited by
- 725
- References
- 11
- FWCI
- 19.90
- Type
- conference paper
- Language
- en
- Open access
- green
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