Lightweight Video Frame Interpolation Based on Bidirectional Attention Module
Yige Li, Yang Han · 2023
Video frame interpolation can enhance the frame rate and improve video quality by synthesizing non-existing intermediate frames between two consecutive frames. Recently, remarkable advances have been made due to the employment of convolutional neural networks. However, most existing methods suffer from motion blur and artifacts when handling the case of large motion and occlusion. To solve the problem, we propose a lightweight but effective deep neural network which is trained end-to-end. Specifically, the bidirectional attention module is first devised to enhance motion-related features representation in both channel and spatial dimensions. Then the synthesis network estimates kernel weights, visibility map and offset vectors to finally generate the interpolation results. Moreover, to compress the model, we introduce the Ghost module to the synthesis network which is verified to be highly effective. Experiment results demonstrate that our proposed architecture performs favorably against state-of-the-art frame interpolation methods on various public video datasets.