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Transformers have been widely used for video processing owing to the multi-head self attention (MHSA) mechanism.
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2018
Cited alongside, same era.
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2019
Cited alongside, same era.
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2019
Cited alongside, same era.
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “SwinIR: Image restoration using swin transformer,” in ICCV Workshops , 2021, pp. 1833–1844
2021
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2021
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2021
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X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in CVPR , 2019, pp. 9308–9316
2019
Cited alongside, same era.
C. Wang, H. Huang, X. Han, and J. Wang, “Video inpainting by jointly learning temporal structure and spatial details,” in AAAI , vol. 33, 2019, pp. 5232–5239
2019
Cited alongside, same era.
Y.-L. Chang, Z. Y. Liu, K.-Y. Lee, and W. Hsu, “Free-form video inpainting with 3D gated convolution and temporal PatchGAN,” in ICCV , 2019, pp. 9066–9075
2019
Cited alongside, same era.
——, “Learnable gated temporal shift module for deep video inpainting,” in BMVC , 2019
2019
Cited alongside, same era.
S. Lee, S. W. Oh, D. Won, and S. J. Kim, “Copy-and-paste networks for deep video inpainting,” in ICCV , 2019, pp. 4413–4421
2019
Cited alongside, same era.
S. W. Oh, S. Lee, J.-Y. Lee, and S. J. Kim, “Onion-peel networks for deep video completion,” in ICCV , 2019, pp. 4403–4412
2019
Cited alongside, same era.
H. Zhang, L. Mai, N. Xu, Z. Wang, J. Collomosse, and H. Jin, “An internal learning approach to video inpainting,” in ICCV , 2019, pp. 2720–2729
2019
Cited alongside, same era.
2021
Later among the works it cites.
L. Ke, Y.-W. Tai, and C.-K. Tang, “Occlusion-aware video object inpainting,” in ICCV , 2021, pp. 14 448–14 458
2021
Later among the works it cites.
H. Ouyang, T. Wang, and Q. Chen, “Internal video inpainting by implicit long-range propagation,” in ICCV , 2021, pp. 14 559–14 568
2021
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021. [Online]. Available: https://openreview.net/forum?id=YicbFdNTTy
2021
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S. Bhojanapalli, A. Chakrabarti, D. Glasner, D. Li, T. Unterthiner, and A. Veit, “Understanding robustness of transformers for image classification,” in ICCV , October 2021, pp. 10 231–10 241
2021
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H. Fan, B. Xiong, K. Mangalam, Y. Li, Z. Yan, J. Malik, and C. Feichtenhofer, “Multiscale vision transformers,” in ICCV , October 2021, pp. 6824–6835
2021
Later among the works it cites.
H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang, “CvT: Introducing convolutions to vision transformers,” in ICCV , October 2021, pp. 22–31
2021
Later among the works it cites.
I. Misra, R. Girdhar, and A. Joulin, “An end-to-end transformer model for 3D object detection,” in ICCV , October 2021, pp. 2906–2917
2021
Later among the works it cites.
X. Wang, S. Zhang, Z. Qing, Y. Shao, Z. Zuo, C. Gao, and N. Sang, “OadTR: Online action detection with transformers,” in ICCV , October 2021, pp. 7565–7575
2021
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H. Wang, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “Max-deeplab: End-to-end panoptic segmentation with mask transformers,” in CVPR , 2021, pp. 5463–5474
2021
Later among the works it cites.
K. Zhang, J. Fu, and D. Liu, “Flow-guided transformer for video inpainting,” in ECCV . Springer, 2022, pp. 74–90
2022
Later among the works it cites.
Z. Li, C.-Z. Lu, J. Qin, C.-L. Guo, and M.-M. Cheng, “Towards an end-to-end framework for flow-guided video inpainting,” in CVPR , 2022, pp. 17 541–17 550
2022
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M. Song, Y. Zhang, and T. O. Aydın, “TempFormer: Temporally consistent transformer for video denoising,” in ECCV . Cham: Springer Nature Switzerland, 2022, pp. 481–496
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Liu, H. Yang, J. Fu, and X. Qian, “Learning trajectory-aware transformer for video super-resolution,” in CVPR , 2022, pp. 5677–5686
2022
Later among the works it cites.
K. Zhang, J. Fu, and D. Liu, “Inertia-guided flow completion and style fusion for video inpainting,” in CVPR , June 2022, pp. 5982–5991
2022
Later among the works it cites.
K. C. Chan, S. Zhou, X. Xu, and C. C. Loy, “BasicVSR++: Improving video super-resolution with enhanced propagation and alignment,” in CVPR , 2022, pp. 5962–5971
2022
Later among the works it cites.
R. Suvorov, E. Logacheva, A. Mashikhin, A. Remizova, A. Ashukha, A. Silvestrov, N. Kong, H. Goka, K. Park, and V. Lempitsky, “Resolution-robust large mask inpainting with fourier convolutions,” in WACV , 2022, pp. 2149–2159
2022
Later among the works it cites.
Z. Qiu, H. Yang, J. Fu, and D. Fu, “Learning spatiotemporal frequency-transformer for compressed video super-resolution,” in ECCV , 2022, pp. 257–273
2022
Later among the works it cites.
J. Huang, Y. Liu, F. Zhao, K. Yan, J. Zhang, Y. Huang, M. Zhou, and Z. Xiong, “Deep fourier-based exposure correction network with spatial-frequency interaction,” in ECCV . Springer, 2022, pp. 163–180
2022
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X. Chu, Z. Tian, B. Zhang, X. Wang, and C. Shen, “Conditional positional encodings for vision transformers,” in ICLR , 2023. [Online]. Available: https://openreview.net/forum?id=3KWnuT-R1bh
2023
Closest in time.
T. Kalluri, D. Pathak, M. Chandraker, and D. Tran, “Flavr: Flow-agnostic video representations for fast frame interpolation,” in WACV , 2023, pp. 2071–2082
2082
Closest in time.