{"id":751951,"date":"2021-06-07T13:36:26","date_gmt":"2021-06-07T20:36:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=751951"},"modified":"2021-06-07T13:36:26","modified_gmt":"2021-06-07T20:36:26","slug":"cdfi-compression-driven-network-design-for-frame-interpolation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cdfi-compression-driven-network-design-for-frame-interpolation\/","title":{"rendered":"CDFI: Compression-Driven Network Design for Frame Interpolation"},"content":{"rendered":"

DNN-based frame interpolation–that generates the intermediate frames given two consecutive frames–typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10X compressed AdaCoF performs similarly as its original counterpart; then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoF. Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithm. Our source code is available on GitHub (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

DNN-based frame interpolation–that generates the intermediate frames given two consecutive frames–typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce 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Ding","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Luming Liang","user_id":38356,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Luming Liang"},{"type":"text","value":"Zhihui Zhu","user_id":0,"rest_url":false},{"type":"text","value":"Ilya 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