FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation
The problem of video inter-frame interpolation is an essential task in the field of image processing. Correctly increasing the number of frames in the recording while maintaining smooth movement allows to improve the quality of played video sequence, enables more effective compression and creating a slow-motion recording. This paper proposes the FastRIFE algorithm, which is some speed improvement of the RIFE (Real-Time Intermediate Flow Estimation) model. The novel method was examined and compared with other recently published algorithms. All source codes are available at https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos
Code (1)
Tasks
Video Frame InterpolationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to …
Optical Flow EstimationVideo Frame InterpolationAn Overflow/Underflow-Free Fixed-Point Bit-Width Optimization Method for OS-ELM Digital Circuit
Currently there has been increasing demand for real-time training on resource-limited IoT devices such as smart sensors, which realizes standalone online adaptation for streaming data without data transfers to remote ser…
Target-aware Image Editing via Cycle-consistent Constraints
Recent pre-trained text-to-image flow models have enabled remarkable progress in text-based image editing. Mainstream approaches adopt a corruption-then-restoration paradigm, where the source image is first corrupted int…
Text-based Image EditingImage ReconstructionRethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via intermediate domains. However, real intermediate domains are often unavailabl…
Domain AdaptationQ-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity. However, effectively leveraging this expressivity for value maximizati…
Reinforcement Learning