{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T07:13:30Z","timestamp":1760426010869,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Tethe Special Foundation for the Development of Strategic Emerging Industries of Shenzhen","award":["JCYJ20170817161056260"],"award-info":[{"award-number":["JCYJ20170817161056260"]}]},{"name":"Tencent Video Cloud"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,17]]},"DOI":"10.1145\/3474085.3475672","type":"proceedings-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T04:52:26Z","timestamp":1634532746000},"page":"5445-5453","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["How Video Super-Resolution and Frame Interpolation Mutually Benefit"],"prefix":"10.1145","author":[{"given":"Chengcheng","family":"Zhou","sequence":"first","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}]},{"given":"Zongqing","family":"Lu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}]},{"given":"Linge","family":"Li","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd., Shenzhen, China"}]},{"given":"Qiangyu","family":"Yan","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd, Shenzhen, China"}]},{"given":"Jing-Hao","family":"Xue","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2021,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-010-0390-2"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Wenbo Bao Wei-Sheng Lai Chao Ma Xiaoyun Zhang Zhiyong Gao and Ming- Hsuan Yang. 2019. Depth-aware video frame interpolation. In CVPR. 3703--3712.  Wenbo Bao Wei-Sheng Lai Chao Ma Xiaoyun Zhang Zhiyong Gao and Ming- Hsuan Yang. 2019. Depth-aware video frame interpolation. In CVPR. 3703--3712.","DOI":"10.1109\/CVPR.2019.00382"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2941941"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Wenbo Bao Xiaoyun Zhang Shangpeng Yan and Zhiyong Gao. 2017. Iterative convolutional neural network for noisy image super-resolution. In ICIP. 4038--4042.  Wenbo Bao Xiaoyun Zhang Shangpeng Yan and Zhiyong Gao. 2017. Iterative convolutional neural network for noisy image super-resolution. In ICIP. 4038--4042.","DOI":"10.1109\/ICIP.2017.8297041"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Xianhang Cheng and Zhenzhong Chen. 2020. Video Frame Interpolation via Deformable Separable Convolution.. In AAAI. 10607--10614.  Xianhang Cheng and Zhenzhong Chen. 2020. Video Frame Interpolation via Deformable Separable Convolution.. In AAAI. 10607--10614.","DOI":"10.1609\/aaai.v34i07.6634"},{"key":"e_1_3_2_2_7_1","volume-title":"Tae Hyun Kim, and Kyoung Mu Lee","author":"Choi Myungsub","year":"2020","unstructured":"Myungsub Choi , Janghoon Choi , Sungyong Baik , Tae Hyun Kim, and Kyoung Mu Lee . 2020 . Scene-Adaptive Video Frame Interpolation via Meta-Learning. In CVPR. 9444--9453. Myungsub Choi, Janghoon Choi, Sungyong Baik, Tae Hyun Kim, and Kyoung Mu Lee. 2020. Scene-Adaptive Video Frame Interpolation via Meta-Learning. In CVPR. 9444--9453."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Myungsub Choi Heewon Kim Bohyung Han Ning Xu and Kyoung Mu Lee. 2020. Channel Attention Is All You Need for Video Frame Interpolation.. In AAAI. 10663--10671.  Myungsub Choi Heewon Kim Bohyung Han Ning Xu and Kyoung Mu Lee. 2020. Channel Attention Is All You Need for Video Frame Interpolation.. In AAAI. 10663--10671.","DOI":"10.1609\/aaai.v34i07.6693"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.316"},{"key":"e_1_3_2_2_10_1","volume-title":"Residual conv-deconv grid network for semantic segmentation. arXiv preprint arXiv:1707.07958","author":"Fourure Damien","year":"2017","unstructured":"Damien Fourure , R\u00e9mi Emonet , Elisa Fromont , Damien Muselet , Alain Tremeau , and Christian Wolf . 2017. Residual conv-deconv grid network for semantic segmentation. arXiv preprint arXiv:1707.07958 ( 2017 ). Damien Fourure, R\u00e9mi Emonet, Elisa Fromont, Damien Muselet, Alain Tremeau, and Christian Wolf. 2017. Residual conv-deconv grid network for semantic segmentation. arXiv preprint arXiv:1707.07958 (2017)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Muhammad Haris Gregory Shakhnarovich and Norimichi Ukita. 2018. Deep back-projection networks for super-resolution. In CVPR. 1664--1673.  Muhammad Haris Gregory Shakhnarovich and Norimichi Ukita. 2018. Deep back-projection networks for super-resolution. In CVPR. 1664--1673.","DOI":"10.1109\/CVPR.2018.00179"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Muhammad Haris Gregory Shakhnarovich and Norimichi Ukita. 2019. Recurrent back-projection network for video super-resolution. In CVPR. 3897--3906.  Muhammad Haris Gregory Shakhnarovich and Norimichi Ukita. 2019. Recurrent back-projection network for video super-resolution. In CVPR. 3897--3906.","DOI":"10.1109\/CVPR.2019.00402"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Muhammad Haris Greg Shakhnarovich and Norimichi Ukita. 2020. Space-Time-Aware Multi-Resolution Video Enhancement. In CVPR. 2859--2868.  Muhammad Haris Greg Shakhnarovich and Norimichi Ukita. 2020. Space-Time-Aware Multi-Resolution Video Enhancement. In CVPR. 2859--2868.","DOI":"10.1109\/CVPR42600.2020.00293"},{"key":"e_1_3_2_2_14_1","volume-title":"RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation. arXiv preprint arXiv:2011.06294","author":"Huang Zhewei","year":"2020","unstructured":"Zhewei Huang , Tianyuan Zhang , Wen Heng , Boxin Shi , and Shuchang Zhou . 2020 . RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation. arXiv preprint arXiv:2011.06294 (2020). Zhewei Huang, Tianyuan Zhang,Wen Heng, Boxin Shi, and Shuchang Zhou. 2020. RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation. arXiv preprint arXiv:2011.06294 (2020)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Takashi Isobe Songjiang Li Xu Jia Shanxin Yuan Gregory Slabaugh Chunjing Xu Ya-Li Li Shengjin Wang and Qi Tian. 2020. Video super-resolution with temporal group attention. In CVPR. 8008--8017.  Takashi Isobe Songjiang Li Xu Jia Shanxin Yuan Gregory Slabaugh Chunjing Xu Ya-Li Li Shengjin Wang and Qi Tian. 2020. Video super-resolution with temporal group attention. In CVPR. 8008--8017.","DOI":"10.1109\/CVPR42600.2020.00803"},{"key":"e_1_3_2_2_16_1","volume-title":"Erik Learned- Miller, and Jan Kautz","author":"Jiang Huaizu","year":"2018","unstructured":"Huaizu Jiang , Deqing Sun , Varun Jampani , Ming-Hsuan Yang , Erik Learned- Miller, and Jan Kautz . 2018 . Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. In CVPR. 9000--9008. Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned- Miller, and Jan Kautz. 2018. Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. In CVPR. 9000--9008."},{"key":"e_1_3_2_2_17_1","volume-title":"Jaeyeon Kang, and Seon Joo Kim.","author":"Jo Younghyun","year":"2018","unstructured":"Younghyun Jo , Seoung Wug Oh , Jaeyeon Kang, and Seon Joo Kim. 2018 . Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In CVPR. 3224--3232. Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. 2018. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In CVPR. 3224--3232."},{"key":"e_1_3_2_2_18_1","volume-title":"Peter Vajda, and Seon Joo Kim.","author":"Kang Jaeyeon","year":"2020","unstructured":"Jaeyeon Kang , Younghyun Jo , Seoung Wug Oh , Peter Vajda, and Seon Joo Kim. 2020 . Deep Space-Time Video Upsampling Networks . arXiv preprint arXiv:2004.02432 (2020). Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh, Peter Vajda, and Seon Joo Kim. 2020. Deep Space-Time Video Upsampling Networks. arXiv preprint arXiv:2004.02432 (2020)."},{"key":"e_1_3_2_2_19_1","volume-title":"FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss.. In AAAI. 11278--11286.","author":"Kim Soo Ye","year":"2020","unstructured":"Soo Ye Kim , Jihyong Oh , and Munchurl Kim . 2020 . FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss.. In AAAI. 11278--11286. Soo Ye Kim, Jihyong Oh, and Munchurl Kim. 2020. FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss.. In AAAI. 11278--11286."},{"key":"e_1_3_2_2_20_1","unstructured":"Hyeongmin Lee Taeoh Kim Tae-young Chung Daehyun Pak Yuseok Ban and Sangyoun Lee. 2020. AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation. In CVPR. 5316--5325.  Hyeongmin Lee Taeoh Kim Tae-young Chung Daehyun Pak Yuseok Ban and Sangyoun Lee. 2020. AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation. In CVPR. 5316--5325."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995614"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Simon Niklaus and Feng Liu. 2018. Context-aware synthesis for video frame interpolation. In CVPR. 1701--1710.  Simon Niklaus and Feng Liu. 2018. Context-aware synthesis for video frame interpolation. In CVPR. 1701--1710.","DOI":"10.1109\/CVPR.2018.00183"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Simon Niklaus Long Mai and Feng Liu. 2017. Video frame interpolation via adaptive separable convolution. In CVPR. 261--270.  Simon Niklaus Long Mai and Feng Liu. 2017. Video frame interpolation via adaptive separable convolution. In CVPR. 261--270.","DOI":"10.1109\/ICCV.2017.37"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/645315.649159"},{"key":"e_1_3_2_2_25_1","volume-title":"Amir Roshan Zamir, and Mubarak Shah","author":"Soomro Khurram","year":"2012","unstructured":"Khurram Soomro , Amir Roshan Zamir, and Mubarak Shah . 2012 . UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012). Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)."},{"key":"e_1_3_2_2_26_1","unstructured":"Hang Su Varun Jampani Deqing Sun Orazio Gallo Erik Learned-Miller and Jan Kautz. 2019. Pixel-adaptive convolutional neural networks. In CVPR. 11166--11175.  Hang Su Varun Jampani Deqing Sun Orazio Gallo Erik Learned-Miller and Jan Kautz. 2019. Pixel-adaptive convolutional neural networks. In CVPR. 11166--11175."},{"key":"e_1_3_2_2_27_1","unstructured":"Deqing Sun Xiaodong Yang Ming-Yu Liu and Jan Kautz. 2018. PWC-Net: CNNs for optical flow using pyramid warping and cost volume. In CVPR. 8934--8943.  Deqing Sun Xiaodong Yang Ming-Yu Liu and Jan Kautz. 2018. PWC-Net: CNNs for optical flow using pyramid warping and cost volume. In CVPR. 8934--8943."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"e_1_3_2_2_29_1","volume-title":"TDAN: Temporally- Deformable Alignment Network for Video Super-Resolution. In CVPR. 3360--3369.","author":"Tian Yapeng","year":"2020","unstructured":"Yapeng Tian , Yulun Zhang , Yun Fu , and Chenliang Xu . 2020 . TDAN: Temporally- Deformable Alignment Network for Video Super-Resolution. In CVPR. 3360--3369. Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. 2020. TDAN: Temporally- Deformable Alignment Network for Video Super-Resolution. In CVPR. 3360--3369."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2958030"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2967596"},{"key":"e_1_3_2_2_32_1","volume-title":"Ke Yu, Chao Dong, and Chen Change Loy.","author":"Wang Xintao","year":"2019","unstructured":"Xintao Wang , Kelvin CK Chan , Ke Yu, Chao Dong, and Chen Change Loy. 2019 . EDVR : Video restoration with enhanced deformable convolutional networks. In CVPRW. 1954--1963. Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. 2019. EDVR: Video restoration with enhanced deformable convolutional networks. In CVPRW. 1954--1963."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Xiaoyu Xiang Yapeng Tian Yulun Zhang Yun Fu Jan P Allebach and Chenliang Xu. 2020. Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution. In CVPR. 3370--3379.  Xiaoyu Xiang Yapeng Tian Yulun Zhang Yun Fu Jan P Allebach and Chenliang Xu. 2020. Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution. In CVPR. 3370--3379.","DOI":"10.1109\/CVPR42600.2020.00343"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-01144-2"},{"key":"e_1_3_2_2_35_1","unstructured":"Peng Yi Zhongyuan Wang Kui Jiang Junjun Jiang and Jiayi Ma. 2019. Progressive fusion video super-resolution network via exploiting non-local spatio temporal correlations. In CVPR. 3106--3115.  Peng Yi Zhongyuan Wang Kui Jiang Junjun Jiang and Jiayi Ma. 2019. Progressive fusion video super-resolution network via exploiting non-local spatio temporal correlations. In CVPR. 3106--3115."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Yulun Zhang Yapeng Tian Yu Kong Bineng Zhong and Yun Fu. 2018. Residual dense network for image super-resolution. In CVPR. 2472--2481.  Yulun Zhang Yapeng Tian Yu Kong Bineng Zhong and Yun Fu. 2018. Residual dense network for image super-resolution. In CVPR. 2472--2481.","DOI":"10.1109\/CVPR.2018.00262"}],"event":{"name":"MM '21: ACM Multimedia Conference","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Virtual Event China","acronym":"MM '21"},"container-title":["Proceedings of the 29th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474085.3475672","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474085.3475672","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:25Z","timestamp":1750193305000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474085.3475672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,17]]},"references-count":36,"alternative-id":["10.1145\/3474085.3475672","10.1145\/3474085"],"URL":"https:\/\/doi.org\/10.1145\/3474085.3475672","relation":{},"subject":[],"published":{"date-parts":[[2021,10,17]]},"assertion":[{"value":"2021-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}