Scene-Adaptive Video Frame Interpolation via Meta-Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Myungsub | - |
dc.contributor.author | Choi, Janghoon | - |
dc.contributor.author | Baik, Sungyong | - |
dc.contributor.author | Kim, Tae Hyun | - |
dc.contributor.author | Lee, Kyoung Mu | - |
dc.date.accessioned | 2022-07-08T00:21:04Z | - |
dc.date.available | 2022-07-08T00:21:04Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145498 | - |
dc.description.abstract | Video frame interpolation is a challenging problem because there are different scenarios for each video depending on the variety of foreground and background motion, frame rate, and occlusion. It is therefore difficult for a single network with fixed parameters to generalize across different videos. Ideally, one could have a different network for each scenario, but this is computationally infeasible for practical applications. In this work, we propose to adapt the model to each video by making use of additional information that is readily available at test time and yet has not been exploited in previous works. We first show the benefits of 'test-time adaptation' through simple fine-tuning of a network, then we greatly improve its efficiency by incorporating meta-learning. We obtain significant performance gains with only a single gradient update without any additional parameters. Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Scene-Adaptive Video Frame Interpolation via Meta-Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Tae Hyun | - |
dc.identifier.doi | 10.1109/CVPR42600.2020.00946 | - |
dc.identifier.scopusid | 2-s2.0-85094860709 | - |
dc.identifier.bibliographicCitation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.9441 - 9450 | - |
dc.relation.isPartOf | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.citation.title | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.citation.startPage | 9441 | - |
dc.citation.endPage | 9450 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceeding | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Benchmarking | - |
dc.subject.keywordPlus | Pattern recognition | - |
dc.subject.keywordPlus | Background motion | - |
dc.subject.keywordPlus | Benchmark datasets | - |
dc.subject.keywordPlus | Its efficiencies | - |
dc.subject.keywordPlus | Meta-learning frameworks | - |
dc.subject.keywordPlus | Metalearning | - |
dc.subject.keywordPlus | Performance Gain | - |
dc.subject.keywordPlus | Scene adaptive | - |
dc.subject.keywordPlus | Single networks | - |
dc.subject.keywordPlus | Interpolation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9156311 | - |
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