all AI news
LADDER: An Efficient Framework for Video Frame Interpolation
April 18, 2024, 4:44 a.m. | Tong Shen, Dong Li, Ziheng Gao, Lu Tian, Emad Barsoum
cs.CV updates on arXiv.org arxiv.org
Abstract: Video Frame Interpolation (VFI) is a crucial technique in various applications such as slow-motion generation, frame rate conversion, video frame restoration etc. This paper introduces an efficient video frame interpolation framework that aims to strike a favorable balance between efficiency and quality. Our framework follows a general paradigm consisting of a flow estimator and a refinement module, while incorporating carefully designed components. First of all, we adopt depth-wise convolution with large kernels in the flow …
abstract applications arxiv balance conversion cs.cv efficiency etc framework general interpolation paper quality rate restoration slow-motion strike type video
More from arxiv.org / cs.CV updates on arXiv.org
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
1 day, 8 hours ago |
arxiv.org
Fingerprint Matching with Localized Deep Representation
1 day, 8 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne