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Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets.
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B. Fernando, T. Tommasi, and T. Tuytelaars · 2015
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Y. Ganin and V. Lempitsky · 2015
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G. Hinton, O. Vinyals, and J. Dean · 2015
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O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
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FaceNet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Face search at scale: 80 million gallery
D. Wang, C. Otto, and A. K. Jain · 2015
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X. Wang, A. Farhadi, and A. Gupta · 2016
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A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, Z. Li, and Y. Qiao · 2016
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D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon · 2016
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Deep deformation network for object landmark localization
X. Yu, F. Zhou, and M. Chandraker · 2016
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T. Zhou, S. Tulsiani, W. Sun, J. Malik, and A. A. Efros · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2017
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Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2017
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Neural aggregation network for video face recognition
J. Yang, P. Ren, D. Zhang, D. Chen, F. Wen, H. Li, and G. Hua · 2017
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