{"type":"ScholarlyArticle","title":"Video Frame Interpolation with Densely Queried Bilateral Correlation","authors":[{"name":"Chang Zhou","slug":"chang-zhou","given":"Chang","family":"Zhou","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Jie Liu","slug":"jie-liu","given":"Jie","family":"Liu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Gangshan Wu","slug":"gangshan-wu","given":"Gangshan","family":"Wu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"}],"abstract":"Video Frame Interpolation (VFI) aims to synthesize non-existent intermediate frames between existent frames. Flow-based VFI algorithms estimate intermediate motion fields to warp the existent frames. Real-world motions' complexity and the reference frame's absence make motion estimation challenging. Many state-of-the-art approaches explicitly model the correlations between two neighboring frames for more accurate motion estimation. In common approaches, the receptive field of correlation modeling at higher resolution depends on the motion fields estimated beforehand. Such receptive field dependency makes common motion estimation approaches poor at coping with small and fast-moving objects. To better model correlations and to produce more accurate motion fields, we propose the Densely Queried Bilateral Correlation (DQBC) that gets rid of the receptive field dependency problem and thus is more friendly to small and fast-moving objects. The motion fields generated with the help of DQBC are further refined and up-sampled with context features. After the motion fields are fixed, a CNN-based SynthNet synthesizes the final interpolated frame. Experiments show that our approach enjoys higher accuracy and less inference time than the state-of-the-art. Source code is available at https://github.com/kinoud/DQBC.","doi":"10.24963/ijcai.2023/198","url":"https://ijcai.org/proceedings/2023/198","pdf":"https://ijcai.org/proceedings/2023/0198.pdf","markdown":"https://ijcai.org/proceedings/2023/198.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/198","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":198,"pages":{"first":1786,"last":1794},"keywords":["Computer Vision: CV: Video analysis and understanding","Computer Vision: CV: Motion and tracking","Computer Vision: CV: Computational photography"],"videos":[],"references":[{"position":1,"raw":"[Baker et al., 2011] Simon Baker, Daniel Scharstein, JP Lewis, Stefan Roth, Michael J Black, and Richard Szeliski. A database and evaluation methodology for optical flow. International journal of computer vision, 92(1):1-31, 2011.","title":"A database and evaluation methodology for optical flow","authors":"Simon Baker, Daniel Scharstein, Stefan Lewis, Michael J Roth, Richard Black, Szeliski","year":"2011","venue":"International journal of computer vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bao et al., 2019] Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. Depth-aware video frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 3703-3712, 2019.","title":"Depth-aware video frame interpolation","authors":"Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen and Zwicker, 2022] Shuhong Chen and Matthias Zwicker. Improving the perceptual quality of 2d anima- tion interpolation. In European Conference on Computer Vision, pages 271-287. Springer, 2022.","title":"Improving the perceptual quality of 2d animation interpolation","authors":"Shuhong Chen, Matthias Zwicker","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng and Chen, 2020] Xianhang Cheng and Zhenzhong Chen. Video frame interpolation via deformable separa- ble convolution. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 10607-10614, 2020.","title":"Video frame interpolation via deformable separable convolution","authors":"Chen ; Cheng, Xianhang Cheng, Zhenzhong Chen","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng and Chen, 2021] Xianhang Cheng and Zhenzhong Chen. Multiple video frame interpolation via enhanced deformable separable convolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.","title":"Multiple video frame interpolation via enhanced deformable separable convolution","authors":"Chen ; Cheng, Xianhang Cheng, Zhenzhong Chen","year":"2021","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chi et al., 2020] Zhixiang Chi, Rasoul Mohammadi Nasiri, Zheng Liu, Juwei Lu, Jin Tang, and Konstantinos N Pla- taniotis. All at once: Temporally adaptive multi-frame in- terpolation with advanced motion modeling. In European Conference on Computer Vision, pages 107-123. Springer, 2020.","title":"All at once: Temporally adaptive multi-frame interpolation with advanced motion modeling","authors":"Zhixiang Chi, Rasoul Mohammadi Nasiri, Zheng Liu, Juwei Lu, Jin Tang, Konstantinos N Plataniotis","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Choi et al., 2020] Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, and Kyoung Mu Lee. Channel attention is all you need for video frame interpolation. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 34, pages 10663-10671, 2020.","title":"Channel attention is all you need for video frame interpolation","authors":"Choi","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Choi et al., 2021] Myungsub Choi, Suyoung Lee, Heewon Kim, and Kyoung Mu Lee. Motion-aware dynamic archi- tecture for efficient frame interpolation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13839-13848, 2021.","title":"Motion-aware dynamic architecture for efficient frame interpolation","authors":"Choi","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Danier et al., 2022] Duolikun Danier, Fan Zhang, and David Bull. St-mfnet: A spatio-temporal multi-flow net- work for frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3521-3531, 2022.","title":"St-mfnet: A spatio-temporal multi-flow network for frame interpolation","authors":"Danier","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Haris et al., 2020] Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita. Space-time-aware multi-resolution video enhancement. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 2859-2868, 2020.","title":"Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita. Space-time-aware multi-resolution video enhancement","authors":"Haris","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Huang et al., 2022] Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time interme- diate flow estimation for video frame interpolation. In Eu- ropean Conference on Computer Vision, pages 624-642. Springer, 2022.","title":"Real-time intermediate flow estimation for video frame interpolation","authors":"Huang","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jiang et al., 2018] Huaizu Jiang, Deqing Sun, Varun Jam- pani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz. Super slomo: High quality estimation of multi- ple intermediate frames for video interpolation. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 9000-9008, 2018.","title":"Super slomo: High quality estimation of multiple intermediate frames for video interpolation","authors":"Jiang","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kalantari et al., 2016] Nima Khademi Kalantari, Ting- Chun Wang, and Ravi Ramamoorthi. Learning-based view synthesis for light field cameras. ACM Transactions on Graphics (TOG), 35(6):1-10, 2016.","title":"Learning-based view synthesis for light field cameras","authors":"Nima Khademi Kalantari, Ting-Chun Wang, Ravi Ramamoorthi","year":"2016","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kim et al., 2020] Soo Ye Kim, Jihyong Oh, and Munchurl Kim. Fisr: Deep joint frame interpolation and super- resolution with a multi-scale temporal loss. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 34, pages 11278-11286, 2020.","title":"Fisr: Deep joint frame interpolation and superresolution with a multi-scale temporal loss","authors":"Kim Soo Ye, Jihyong Oh, Munchurl Kim","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kong et al., 2022] Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, and Jie Yang. Ifrnet: Intermediate feature refine network for efficient frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 1969-1978, 2022.","title":"Ifrnet: Intermediate feature refine network for efficient frame interpolation","authors":"Kong","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lee et al., 2020] Hyeongmin Lee, Taeoh Kim, Tae-young Chung, Daehyun Pak, Yuseok Ban, and Sangyoun Lee. Adacof: Adaptive collaboration of flows for video frame interpolation. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 5316-5325, 2020.","title":"Adacof: Adaptive collaboration of flows for video frame interpolation","authors":"Lee","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2017] Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video frame synthesis using deep voxel flow. In Proceedings of the IEEE interna- tional conference on computer vision, pages 4463-4471, 2017.","title":"Video frame synthesis using deep voxel flow","authors":"Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala","year":"2017","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2019] Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, and Yung-Yu Chuang. Deep video frame interpolation us- ing cyclic frame generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8794-8802, 2019.","title":"Deep video frame interpolation using cyclic frame generation","authors":"Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, Yung-Yu Chuang","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2020] Yihao Liu, Liangbin Xie, Li Siyao, Wenxiu Sun, Yu Qiao, and Chao Dong. Enhanced quadratic video interpolation. In European Conference on Computer Vi- sion, pages 41-56. Springer, 2020.","title":"Enhanced quadratic video interpolation","authors":"Yihao Liu, Liangbin Xie, Li Siyao, Wenxiu Sun, Yu Qiao, Chao Dong","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Loshchilov and Hutter, 2017] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lu et al., 2022] Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, and Jiaya Jia. Video frame interpolation with transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3532- 3542, 2022.","title":"Video frame interpolation with transformer","authors":"Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, Jiaya Jia","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Meyer et al., 2015] Simone Meyer, Oliver Wang, Henning Zimmer, Max Grosse, and Alexander Sorkine-Hornung. Phase-based frame interpolation for video. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1410-1418, 2015.","title":"Phase-based frame interpolation for video","authors":"Simone Meyer, Oliver Wang, Henning Zimmer, Max Grosse, Alexander Sorkine-Hornung","year":"2015","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Meyer et al., 2018] Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus Gross, and Christopher Schroers. Phasenet for video frame interpolation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 498-507, 2018.","title":"Phasenet for video frame interpolation","authors":"Simone Meyer, Abdelaziz Djelouah, Brian Mcwilliams, Alexander Sorkine-Hornung, Markus Gross, Christopher Schroers","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Niklaus and Liu, 2018] Simon Niklaus and Feng Liu. Context-aware synthesis for video frame interpolation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701-1710, 2018.","title":"Context-aware synthesis for video frame interpolation","authors":"Liu Niklaus, Simon Niklaus, Feng Liu","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Niklaus et al., 2017a] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive convolution. In Proceedings of the IEEE Conference on Computer Vi- sion and Pattern Recognition, pages 670-679, 2017.","title":"Video frame interpolation via adaptive convolution","authors":"Niklaus","year":"2017","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Niklaus et al., 2017b] Simon Niklaus, Long Mai, and Feng Liu. Video frame interpolation via adaptive separable con- volution. In Proceedings of the IEEE International Con- ference on Computer Vision, pages 261-270, 2017.","title":"Video frame interpolation via adaptive separable convolution","authors":"Niklaus","year":"2017","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Park et al., 2020] Junheum Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim. Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation. In Eu- ropean Conference on Computer Vision, pages 109-125. Springer, 2020.","title":"Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation","authors":"Park","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Park et al., 2021] Junheum Park, Chul Lee, and Chang-Su Kim. Asymmetric bilateral motion estimation for video frame interpolation. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 14539- 14548, 2021.","title":"Asymmetric bilateral motion estimation for video frame interpolation","authors":"Park","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Peleg et al., 2019] Tomer Peleg, Pablo Szekely, Doron Sabo, and Omry Sendik. Im-net for high resolution video frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2398-2407, 2019.","title":"Im-net for high resolution video frame interpolation","authors":"Tomer Peleg, Pablo Szekely, Doron Sabo, Omry Sendik","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Reda et al., 2022] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Cur- less. Film: Frame interpolation for large motion. In Eu- ropean Conference on Computer Vision, pages 250-266. Springer, 2022.","title":"Film: Frame interpolation for large motion","authors":"Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, Brian Curless","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shen et al., 2022] Wang Shen, Cheng Ming, Wenbo Bao, Guangtao Zhai, Li Chenn, and Zhiyong Gao. Enhanced deep animation video interpolation. In 2022 IEEE Inter- national Conference on Image Processing (ICIP), pages 31-35. IEEE, 2022.","title":"Enhanced deep animation video interpolation","authors":"Wang Shen, Cheng Ming, Wenbo Bao, Guangtao Zhai, Li Chenn, Zhiyong Gao","year":"2022","venue":"2022 IEEE International Conference on Image Processing (ICIP)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Shi et al., 2022] Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, and Ming-Hsuan Yang. Video frame interpola- tion transformer. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 17482-17491, 2022.","title":"Video frame interpolation transformer","authors":"Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, Ming-Hsuan Yang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Sim et al., 2021] Hyeonjun Sim, Jihyong Oh, and Munchurl Kim. Xvfi: Extreme video frame interpolation. In Pro- ceedings of the IEEE/CVF International Conference on Computer Vision, pages 14489-14498, 2021.","title":"Munchurl Kim. Xvfi: Extreme video frame interpolation","authors":"Sim","year":"2021","venue":"Pro-ceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Siyao et al., 2021] Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, and Zi- wei Liu. Deep animation video interpolation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6587-6595, 2021.","title":"Deep animation video interpolation in the wild","authors":"Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, Ziwei Liu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Soomro et al., 2012] Khurram Soomro, Amir Roshan Za- mir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.","title":"Ucf101: A dataset of 101 human actions classes from videos in the wild","authors":"Khurram Soomro, Mubarak Amir Roshan Zamir, Shah","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Tulyakov et al., 2021] Stepan Tulyakov, Daniel Gehrig, Stamatios Georgoulis, Julius Erbach, Mathias Gehrig, Yuanyou Li, and Davide Scaramuzza. Time lens: Event- based video frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16155-16164, 2021.","title":"Time lens: Eventbased video frame interpolation","authors":"Tulyakov","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2018] Chao-Yuan Wu, Nayan Singhal, and Philipp Krahenbuhl. Video compression through image interpolation. In Proceedings of the European conference on computer vision (ECCV), pages 416-431, 2018.","title":"Video compression through image interpolation","authors":"Chao-Yuan, Nayan Wu, Philipp Singhal, Krahenbuhl","year":"2018","venue":"Proceedings of the European conference on computer vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2022] Yue Wu, Qiang Wen, and Qifeng Chen. Optimizing video prediction via video frame interpola- tion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17814- 17823, 2022.","title":"Optimizing video prediction via video frame interpolation","authors":"Yue Wu, Qiang Wen, Qifeng Chen","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xiang et al., 2020] Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P Allebach, and Chenliang Xu. Zoom- ing slow-mo: Fast and accurate one-stage space-time video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3370-3379, 2020.","title":"Zooming slow-mo: Fast and accurate one-stage space-time video super-resolution","authors":"Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P Allebach, Chenliang Xu","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xu et al., 2019] Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, and Ming-Hsuan Yang. Quadratic video interpola- tion. Advances in Neural Information Processing Systems, 32, 2019.","title":"Quadratic video interpolation","authors":"Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xue et al., 2019] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow. International Journal of Com- puter Vision, 127(8):1106-1125, 2019.","title":"Video enhancement with task-oriented flow","authors":"Xue","year":"2019","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yu et al., 2022] Zhiyang Yu, Yu Zhang, Xujie Xiang, Dongqing Zou, Xijun Chen, and Jimmy S Ren. Deep bayesian video frame interpolation. In European Confer- ence on Computer Vision, pages 144-160. Springer, 2022.","title":"Deep bayesian video frame interpolation","authors":"Zhiyang Yu, Yu Zhang, Xujie Xiang, Dongqing Zou, Xijun Chen, Jimmy S Ren","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou et al., 2016] Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, and Alexei A Efros. View synthesis by appearance flow. In European conference on computer vision, pages 286-301. Springer, 2016.","title":"View synthesis by appearance flow","authors":"Zhou","year":"2016","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}