Sparsity-guided Network Design for Frame Interpolation

• URL: http://arxiv.org/abs/2209.04551v1
• Date: Fri, 9 Sep 2022 23:13:25 GMT
• Title: Sparsity-guided Network Design for Frame Interpolation
• Authors: Tianyu Ding, Luming Liang, Zhihui Zhu, Tianyi Chen, Ilya Zharkov
• Abstract summary: We present a compression-driven network design for frame-based algorithms. We leverage model pruning through sparsity-inducing optimization to greatly reduce the model size. We achieve a considerable performance gain with a quarter of the size of the original AdaCoF.
• Score: 39.828644638174225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: DNN-based frame interpolation, which generates intermediate frames from two consecutive frames, is often dependent on model architectures with a large number of features, preventing their deployment on systems with limited resources, such as mobile devices. We present a compression-driven network design for frame interpolation that leverages model pruning through sparsity-inducing optimization to greatly reduce the model size while attaining higher performance. Concretely, we begin by compressing the recently proposed AdaCoF model and demonstrating that a 10 times compressed AdaCoF performs similarly to its original counterpart, where different strategies for using layerwise sparsity information as a guide are comprehensively investigated under a variety of hyperparameter settings. We then enhance this compressed model by introducing a multi-resolution warping module, which improves visual consistency with multi-level details. As a result, we achieve a considerable performance gain with a quarter of the size of the original AdaCoF. In addition, our model performs favorably against other state-of-the-art approaches on a wide variety of datasets. We note that the suggested compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithms. The source code is available at https://github.com/tding1/CDFI.

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