Video frame interpolation involves the synthesis of new frames from existing
ones. Convolutional neural networks (CNNs) have been at the forefront of the
recent advances in this field. One popular CNN-based approach involves the
application of generated kernels to the input frames to obtain an interpolated
frame. Despite all the benefits interpolation methods offer, many of these
networks require a lot of parameters, with more parameters meaning a heavier
computational burden. Reducing the size of the model typically impacts
performance negatively. This paper presents a method for parameter reduction
for a popular flow-less kernel-based network (Adaptive Collaboration of Flows).
Through our technique of removing the layers that require the most parameters
and replacing them with smaller encoders, we reduce the number of parameters of
the network and even achieve better performance compared to the original
method. This is achieved by deploying rotation to force each individual encoder
to learn different features from the input images. Ablations are conducted to
justify design choices and an evaluation on how our method performs on
full-length videos is presented.

本研究提出了一种通过删除需要最多参数的层并将其替换成更小的编码器的方法来减少流场无关内核网络的参数数量的技术，以通过强制旋转来使每个编码器从输入图像中学习不同的特征，从而实现比原来的方法更好的性能。

基于多编码器网络的核插值结构参数减少

Multi-encoder Network for Parameter Reduction of a Kernel-based  Interpolation Architecture

Video frame interpolation is one of the most challenging tasks in video
processing research. Recently, many studies based on deep learning have been
suggested. Most of these methods focus on finding locations with useful
information to estimate each output pixel using their own frame warping
operations. However, many of them have Degrees of Freedom (DoF) limitations and
fail to deal with the complex motions found in real world videos. To solve this
problem, we propose a new warping module named Adaptive Collaboration of Flows
(AdaCoF). Our method estimates both kernel weights and offset vectors for each
target pixel to synthesize the output frame. AdaCoF is one of the most
generalized warping modules compared to other approaches, and covers most of
them as special cases of it. Therefore, it can deal with a significantly wide
domain of complex motions. To further improve our framework and synthesize more
realistic outputs, we introduce dual-frame adversarial loss which is applicable
only to video frame interpolation tasks. The experimental results show that our
method outperforms the state-of-the-art methods for both fixed training set
environments and the Middlebury benchmark.

提出了一种新的适应性流协作分组方法（Adaptive Collaboration of Flows，AdaCoF），可以综合处理复杂运动的广泛领域，加入双帧对抗性损失，用于视频帧内插计算，实验结果表明优于现有的最佳方法。