| A systematic study of the class imbalance problem in convolutional neural networks M Buda, A Maki, MA Mazurowski Neural Networks 106, 249-259, 2018 | 2784 | 2018 |
| 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 | 551 | 2015 |
| 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 | 545 | 2016 |
| [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 | 545 | 2016 |
| Visual Instance Retrieval with Deep Convolutional Networks A Sharif Razavian, J Sullivan, S Carlsson, A Maki arXiv preprint arXiv:1412.6574, 2014 | 545* | 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 | 464 | 2017 |
| 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 | 402 | 2016 |
| 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 | 402 | 2016 |
| Towards a simulation driven stereo vision system M Peris, A Maki, S Martull, Y Ohkawa, K Fukui 21st International Conference on Pattern Recognition, 2012 | 145 | 2012 |
| 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 | 143 | 2019 |
| 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 | 142 | 2017 |
| Image processing apparatus and image processing method A Maki, M Watanabe, N Matsuda, C Wiles US Patent 6,072,903, 2000 | 138 | 2000 |
| 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 | 109 | 1996 |
| 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 | 103 | 2014 |
| 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 | 100 | 2019 |
| Attentional scene segmentation: integrating depth and motion A Maki, P Nordlund, JO Eklundh Computer Vision and Image Understanding 78 (3), 351-373, 2000 | 99 | 2000 |
| Attentional scene segmentation: integrating depth and motion A Maki, P Nordlund, JO Eklundh Computer Vision and Image Understanding 78 (3), 351-373, 2000 | 99 | 2000 |
| 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 | 97 | 2015 |
| 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 |
| Towards an active visual observer T Uhlin, P Nordlund, A Maki, JO Eklundh Proceedings of IEEE International Conference on Computer Vision, 679-686, 1995 | 83 | 1995 |
