ST-MFNET Mini: Knowledge Distillation-Driven Frame Interpolation-Reference-Cited by-同舟云学术

ST-MFNET Mini: Knowledge Distillation-Driven Frame Interpolation

Published:2023-10-08 Issue: Volume: Page:
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Container-title:2023 IEEE International Conference on Image Processing (ICIP)
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Author:

Morris Crispian¹,Danier Duolikun¹,Zhang Fan¹,Anantrasirichai Nantheera¹,Bull David R.¹

Affiliation:

1. University of Bristol,Bristol Vision Institute,Bristol,UK,BS1 5DD

Funder

University of Bristol

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10221937/10221892/10222892.pdf?arnumber=10222892

Reference33 articles.

1. FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation

2. MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

3. Enhancing Deformable Convolution Based Video Frame Interpolation with Coarse-To-Fine 3d Cnn

4. Flavr: Flow-agnostic video representations for fast frame interpolation;kalluri;2012 arXiv preprint arXiv,2020

5. BMBC: Bilateral motion estimation with bilateral cost volume for video interpolation;park;Computer Vision–ECCV 2020 16th European Conference Glasgow UK August 23–28 2020 Proceedings Part XIV 16,2020

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