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The existing research in action recognition is mostly focused on high-quality videos where the action is distinctly visible.
S. Oh, A. Hoogs, A. Perera, N. Cuntoor, C.-C. Chen, J. T. Lee, S. Mukherjee, J. Aggarwal, H. Lee, L. Davis et al. , “A large-scale benchmark dataset for event recognition in surveillance video,” in CVPR 2011 . IEEE, 2011, pp. 3153–3160
2011
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H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “HMDB: a large video database for human motion recognition,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2011
2011
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2012
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B. Schiele, “A database for fine grained activity detection of cooking activities,” in Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , ser. CVPR ’12, 2012, pp. 1194–1201
2012
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A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in CVPR , 2014
2014
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Y.-G. Jiang, J. Liu, A. Roshan Zamir, G. Toderici, I. Laptev, M. Shah, and R. Sukthankar, “THUMOS challenge: Action recognition with a large number of classes,” http://crcv.ucf.edu/THUMOS14/ , 2014
2014
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D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 4489–4497
2015
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J. Dai, B. Saghafi, J. Wu, J. Konrad, and P. Ishwar, “Towards privacy-preserving recognition of human activities,” 2015 IEEE International Conference on Image Processing (ICIP) , pp. 4238–4242, 2015
2015
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Y. Huang, W. Wang, and L. Wang, “Bidirectional recurrent convolutional networks for multi-frame super-resolution,” in Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds., 2015, pp. 235–243
2015
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C. Xu, S.-H. Hsieh, C. Xiong, and J. J
2015
Earlier work this paper cites.
B. G. Fabian Caba Heilbron, Victor Escorcia and J. C. Niebles, “Activitynet: A large-scale video benchmark for human activity understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 961–970
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR , 2015
2015
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2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 , 2016, pp. 770–778
2016
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L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool, “Temporal segment networks: Towards good practices for deep action recognition,” in European conference on computer vision . Springer, 2016, pp. 20–36
2016
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Z. Wang, S. Chang, Y. Yang, D. Liu, and T. S. Huang, “Studying very low resolution recognition using deep networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
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C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 2, pp. 295–307, Feb. 2016
2016
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J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR Oral) , June 2016
2016
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A. Kappeler, S. Yoo, Q. Dai, and A. Katsaggelos, “Video super-resolution with convolutional neural networks,” IEEE Transactions on Computational Imaging , vol. 2, pp. 1–1, 06 2016
2016
Cited alongside, same era.
W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
Cited alongside, same era.
G. A. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta, “Hollywood in homes: Crowdsourcing data collection for activity understanding,” in European Conference on Computer Vision , 2016
2016
Cited alongside, same era.
2016
2018
Later among the works it cites.
C. Gu, C. Sun, D. A. Ross, C. Vondrick, C. Pantofaru, Y. Li, S. Vijayanarasimhan, G. Toderici, S. Ricco, R. Sukthankar et al. , “Ava: A video dataset of spatio-temporally localized atomic visual actions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 6047–6056
2018
Later among the works it cites.
M. Xu, A. Sharghi, X. Chen, and D. J. Crandall, “Fully-coupled two-stream spatiotemporal networks for extremely low resolution action recognition,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) , vol. 00, Mar 2018, pp. 1607–1615
2018
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K. Hara, H. Kataoka, and Y. Satoh, “Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 6546–6555
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Cited alongside, same era.
A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill , 2016. [Online]. Available: http://distill.pub/2016/deconv-checkerboard
2016
Cited alongside, same era.
2017
Cited alongside, same era.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? A new model and the kinetics dataset,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017, pp. 4724–4733
2017
Cited alongside, same era.
J. Chen, J. Wu, J. Konrad, and P. Ishwar, “Semi-coupled two-stream fusion convnets for action recognition at extremely low resolutions,” in 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, CA, USA, March 24-31, 2017 , 2017, pp. 139–147
2017
Cited alongside, same era.
M. S. Ryoo, B. Rothrock, C. Fleming, and H. J. Yang, “Privacy-preserving human activity recognition from extreme low resolution,” in AAAI , 2017
2017
Cited alongside, same era.
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Deep laplacian pyramid networks for fast and accurate super-resolution,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Cited alongside, same era.
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, July 21-26, 2017 , 2017, pp. 1132–1140
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
Y. Bai, Y. Zhang, M. Ding, and B. Ghanem, “Finding tiny faces in the wild with generative adversarial network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Later among the works it cites.
M. S. Ryoo, K. Kim, and H. J. Yang, “Extreme low resolution activity recognition with multi-siamese embedding learning,” in AAAI , 2018
2018
Later among the works it cites.
J. Li, F. Fang, K. Mei, and G. Zhang, “Multi-scale residual network for image super-resolution,” in The European Conference on Computer Vision (ECCV) , September 2018
2018
Later among the works it cites.
M. S. M. Sajjadi, R. Vemulapalli, and M. Brown, “Frame-Recurrent Video Super-Resolution,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
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Y. Jo, S. Wug Oh, J. Kang, and S. Joo Kim, “Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
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T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in ICLR . OpenReview.net, 2018
2018
Later among the works it cites.
C. Gu, C. Sun, D. A. Ross, C. Vondrick, C. Pantofaru, Y. Li, S. Vijayanarasimhan, G. Toderici, S. Ricco, R. Sukthankar, C. Schmid, and J. Malik, “Ava: A video dataset of spatio-temporally localized atomic visual actions,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
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D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray, “Scaling egocentric vision: The epic-kitchens dataset,” in European Conference on Computer Vision (ECCV) , 2018
2018
Later among the works it cites.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. C. Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in The European Conference on Computer Vision Workshops (ECCVW) , September 2018
2018
Later among the works it cites.
H. Zhang, D. Liu, and Z. Xiong, “Two-stream action recognition-oriented video super-resolution,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Later among the works it cites.
P. Abolghasemi, A. Mazaheri, M. Shah, and L. Boloni, “Pay attention!-robustifying a deep visuomotor policy through task-focused visual attention,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4254–4262
2019
Later among the works it cites.
X. Deng, R. Yang, M. Xu, and P. L. Dragotti, “Wavelet domain style transfer for an effective perception-distortion tradeoff in single image super-resolution,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Later among the works it cites.
P. Yi, Z. Wang, K. Jiang, J. Jiang, and J. Ma, “Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
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