Radu Timofte
Radu Timofte
ETH Zurich (previously at KU Leuven)
Verified email at vision.ee.ethz.ch - Homepage
Title
Cited by
Cited by
Year
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
R Timofte, V De Smet, L Van Gool
ACCV 2014, 2014
9532014
Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
R Timofte, V De Smet, L Van Gool
International Conference on Computer Vision (ICCV), 2013
8882013
Pedestrian detection at 100 frames per second
R Benenson, M Mathias, R Timofte, L Van Gool
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2903-2910, 2012
6542012
The German traffic sign recognition benchmark: a multi-class classification competition
J Stallkamp, M Schlipsing, J Salmen, C Igel
The 2011 international joint conference on neural networks, 1453-1460, 2011
5012011
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
E Agustsson, R Timofte
CVPR Workshops (NTIRE 2017), 2017
3792017
Ntire 2017 challenge on single image super-resolution: Methods and results
R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang, B Lim, S Son, ...
Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE …, 2017
3652017
Hough transform and 3D SURF for robust three dimensional classification
J Knopp, M Prasad, G Willems, R Timofte, L Van Gool
European Conference on Computer Vision, 589-602, 2010
3622010
Deep expectation of real and apparent age from a single image without facial landmarks
R Rothe, R Timofte, L Van Gool
International Journal of Computer Vision (IJCV), 2018
3302018
Multi-view traffic sign detection, recognition, and 3D localisation
R Timofte, K Zimmermann, L Van Gool
Machine vision and applications 25 (3), 633-647, 2014
3092014
Multi-view traffic sign detection, recognition, and 3d localisation
R Timofte, K Zimmermann, L Van Gool
IEEE Workshop on Applications of Computer Vision (WACV), 2009
309*2009
DEX: Deep EXpectation of apparent age from a single image
R Rothe, R Timofte, L Van Gool
ICCV Workshops, 2015
3042015
Traffic Sign Recognition–How far are we from the solution?
M Mathias, R Timofte, R Benenson, L Van Gool
IJCNN 2013, 2013
2672013
Seven ways to improve example-based single image super resolution
R Timofte, R Rothe, L Van Gool
CVPR 2016, 2016
2262016
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
E Agustsson, F Mentzer, M Tschannen, L Cavigelli, R Timofte, L Benini, ...
NIPS, 2017
165*2017
Fast optical flow using dense inverse search
T Kroeger, R Timofte, D Dai, L Van Gool
ECCV 2016, 2016
1412016
Jointly Optimized Regressors for Image Super-resolution
D Dai, R Timofte, L Van Gool
Computer Graphics Forum 34 (2), 95-104, 2015
1342015
Handling Occlusions with Franken-classifiers
M Mathias, R Benenson, R Timofte, L Van Gool
International Conference on Computer Vision (ICCV), 2013
1282013
Conditional Probability Models for Deep Image Compression
F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool
CVPR 2018, 2018
1102018
Generative adversarial networks for extreme learned image compression
E Agustsson, M Tschannen, F Mentzer, R Timofte, LV Gool
Proceedings of the IEEE International Conference on Computer Vision, 221-231, 2019
1092019
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
A Ignatov, N Kobyshev, R Timofte, K Vanhoey, L Van Gool
ICCV 2017, 2017
1062017
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