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Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms in video inpainting (VI).
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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A benchmark dataset and evaluation methodology for video object segmentation
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Deformable convolutional networks
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Learning blind video temporal consistency
Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, and Ming-Hsuan Yang · 2018
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Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Simon Meister, Junhwa Hur, and Stefan Roth · 2018
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Video-to-video synthesis
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
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YouTube-VOS: Sequence-to-sequence video object segmentation
Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, and Thomas Huang · 2018
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Free-form video inpainting with 3d gated convolution and temporal patchgan
Ya-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee, and Winston Hsu · 2019
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Learnable gated temporal shift module for deep video inpainting
Ya-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee, and Winston Hsu · 2019
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Deep video inpainting
Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon · 2019
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Copy-and-paste networks for deep video inpainting
Sungho Lee, Seoung Wug Oh, DaeYeun Won, and Seon Joo Kim · 2019
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Onion-peel networks for deep video completion
Seoung Wug Oh, Sungho Lee, Joon-Young Lee, and Seon Joo Kim · 2019
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Video inpainting by jointly learning temporal structure and spatial details
Chuan Wang, Haibin Huang, Xiaoguang Han, and Jue Wang · 2019
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Deep flow-guided video inpainting
Rui Xu, Xiaoxiao Li, Bolei Zhou, and Chen Change Loy · 2019
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An internal learning approach to video inpainting
Haotian Zhang, Long Mai, Ning Xu, Zhaowen Wang, John Collomosse, and Hailin Jin · 2019
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Spatio-temporal filter adaptive network for video deblurring
Shangchen Zhou, Jiawei Zhang, Jinshan Pan, Haozhe Xie, Wangmeng Zuo, and Jimmy Ren · 2019
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Deformable convnets v2: More deformable, better results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2019
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Flow-edge guided video completion
Chen Gao, Ayush Saraf, Jia-Bin Huang, and Johannes Kopf · 2020
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Proposal-based video completion
Yuan-Ting Hu, Heng Wang, Nicolas Ballas, Kristen Grauman, and Alexander G Schwing · 2020
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Short-term and long-term context aggregation network for video inpainting
Ang Li, Shanshan Zhao, Xingjun Ma, Mingming Gong, Jianzhong Qi, Rui Zhang, Dacheng Tao, and Ramamohanarao Kotagiri · 2020
Focal attention for long-range interactions in vision transformers
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, and Jianfeng Gao · 2021
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Progressive temporal feature alignment network for video inpainting
Xueyan Zou, Linjie Yang, Ding Liu, and Yong Jae Lee · 2021
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DeViT: Deformed vision transformers in video inpainting
Jiayin Cai, Changlin Li, Xin Tao, Chun Yuan, and Yu-Wing Tai · 2022
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BasicVSR++: Improving video super-resolution with enhanced propagation and alignment
Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy · 2022
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Investigating tradeoffs in real-world video super-resolution
Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy · 2022
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On the generalization of BasicVSR++ to video deblurring and denoising
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Learning joint spatial-temporal transformations for video inpainting
Yanhong Zeng, Jianlong Fu, and Hongyang Chao · 2020
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BasicVSR: The search for essential components in video super-resolution and beyond
Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy · 2021
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Occlusion-aware video object inpainting
Lei Ke, Yu-Wing Tai, and Chi-Keung Tang · 2021
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Decoupled spatial-temporal transformer for video inpainting
Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, and Hongsheng Li · 2021
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Fuseformer: Fusing fine-grained information in transformers for video inpainting
Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, and Hongsheng Li · 2021
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Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy · 2022
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SpViT: Enabling faster vision transformers via soft token pruning
Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Wei Niu, Mengshu Sun, Bin Ren, Minghai Qin, Hao Tang, and Yanzhi Wang · 2022
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Towards an end-to-end framework for flow-guided video inpainting
Zhen Li, Cheng-Ze Lu, Jianhua Qin, Chun-Le Guo, and Ming-Ming Cheng · 2022
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Not all patches are what you need: Expediting vision transformers via token reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie · 2022
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AdaViT: Adaptive vision transformers for efficient image recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen, Shiyi Lan, Zuxuan Wu, Yu-Gang Jiang, and Ser-Nam Lim · 2022
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DLFormer: Discrete latent transformer for video inpainting
Jingjing Ren, Qingqing Zheng, Yuanyuan Zhao, Xuemiao Xu, and Chen Li · 2022
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A-ViT: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2022
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Flow-guided transformer for video inpainting
Kaidong Zhang, Jingjing Fu, and Dong Liu · 2022
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Inertia-guided flow completion and style fusion for video inpainting
Kaidong Zhang, Jingjing Fu, and Dong Liu · 2022
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