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Atsuto Maki
Atsuto Maki
Professor of Computer Science, KTH Royal Institute of Technology
Verified email at kth.se - Homepage
Title
Cited by
Cited by
Year
A systematic study of the class imbalance problem in convolutional neural networks
M Buda, A Maki, MA Mazurowski
Neural Networks 106, 249-259, 2018
26912018
From generic to specific deep representations for visual recognition
H Azizpour, A Sharif Razavian, J Sullivan, A Maki, S Carlsson
Proceedings of the IEEE conference on computer vision and pattern …, 2015
5462015
Visual instance retrieval with deep convolutional networks
AS Razavian, J Sullivan, S Carlsson, A Maki
ITE Transactions on Media Technology and Applications 4 (3), 251-258, 2016
5412016
[Paper] Visual Instance Retrieval with Deep Convolutional Networks
AS Razavian, J Sullivan, S Carlsson, A Maki
ITE Transactions on Media Technology and Applications 4 (3), 251-258, 2016
5412016
Visual Instance Retrieval with Deep Convolutional Networks
A Sharif Razavian, J Sullivan, S Carlsson, A Maki
arXiv preprint arXiv:1412.6574, 2014
541*2014
Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures?
J Olczak, N Fahlberg, A Maki, AS Razavian, A Jilert, A Stark, ...
Acta orthopaedica 88 (6), 581-586, 2017
4472017
Factors of transferability for a generic convnet representation
H Azizpour, AS Razavian, J Sullivan, A Maki, S Carlsson
IEEE transactions on pattern analysis and machine intelligence 38 (9), 1790-1802, 2016
3982016
Factors of transferability for a generic convnet representation
H Azizpour, AS Razavian, J Sullivan, A Maki, S Carlsson
IEEE transactions on pattern analysis and machine intelligence 38 (9), 1790-1802, 2016
3982016
Towards a simulation driven stereo vision system
M Peris, A Maki, S Martull, Y Ohkawa, K Fukui
21st International Conference on Pattern Recognition, 2012
1442012
Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks
M Valan, K Makonyi, A Maki, D Vondráček, F Ronquist
Systematic biology, 2019
1402019
Deep predictive policy training using reinforcement learning
A Ghadirzadeh, A Maki, D Kragic, M Björkman
Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International …, 2017
1402017
Image processing apparatus and image processing method
A Maki, M Watanabe, N Matsuda, C Wiles
US Patent 6,072,903, 2000
1382000
Difference sphere: an approach to near light source estimation
T Takai, K Niinuma, A Maki, T Matsuyama
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision …, 2004
120*2004
A computational model of depth-based attention
A Maki, P Nordlund, JO Eklundh
Proceedings of 13th International Conference on Pattern Recognition 4, 734-739, 1996
1091996
Demisting the Hough transform for 3D shape recognition and registration
OJ Woodford, MT Pham, A Maki, F Perbet, B Stenger
International Journal of Computer Vision 106 (3), 332-341, 2014
1002014
Attentional scene segmentation: integrating depth and motion
A Maki, P Nordlund, JO Eklundh
Computer Vision and Image Understanding 78 (3), 351-373, 2000
992000
Attentional scene segmentation: integrating depth and motion
A Maki, P Nordlund, JO Eklundh
Computer Vision and Image Understanding 78 (3), 351-373, 2000
992000
Difference subspace and its generalization for subspace-based methods
K Fukui, A Maki
IEEE transactions on pattern analysis and machine intelligence 37 (11), 2164 …, 2015
972015
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
D Feng, X Wei, L Rosenbaum, A Maki, K Dietmayer
arXiv preprint arXiv:1901.10609, 2019
952019
Geotensity: Combining motion and lighting for 3d surface reconstruction
A Maki, M Watanabe, C Wiles
International Journal of Computer Vision 48 (2), 75-90, 2002
90*2002
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