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.
翻译:动态神经网络(CNNs)一直处于该领域最新进展的前列。一个以CNN为基础的流行方法是将生成的内核应用到输入框架,以获得一个内插框架。尽管这些内插方法带来种种好处,但其中许多网络需要大量的参数,而更多的参数意味着更重的计算负担。缩小模型的大小通常会对性能产生消极影响。本文为一个流行的无流动内核网络(动态流动协作)提供了一个降低参数的方法。通过我们去除最需要参数的层,用较小的编码器取代这些层的技术,我们减少了网络参数的数量,甚至取得了比原方法更好的业绩。这是通过部署轮换,迫使每个单个编码器从输入图像中学习不同的特征来实现的。进行了调整,以证明设计选择的理由,并评估我们如何用全长视频进行的方法。