| Wide residual networks S Zagoruyko arXiv preprint arXiv:1605.07146, 2016 | 9544 | 2016 |
| Unsupervised representation learning by predicting image rotations S Gidaris, P Singh, N Komodakis arXiv preprint arXiv:1803.07728, 2018 | 3945 | 2018 |
| Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer S Zagoruyko, N Komodakis arXiv preprint arXiv:1612.03928, 2016 | 3145 | 2016 |
| Learning to compare image patches via convolutional neural networks S Zagoruyko, N Komodakis Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1899 | 2015 |
| Dynamic edge-conditioned filters in convolutional neural networks on graphs M Simonovsky, N Komodakis Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1542 | 2017 |
| Dynamic few-shot visual learning without forgetting S Gidaris, N Komodakis Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1374 | 2018 |
| Graphvae: Towards generation of small graphs using variational autoencoders M Simonovsky, N Komodakis Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 1005 | 2018 |
| Object detection via a multi-region and semantic segmentation-aware cnn model S Gidaris, N Komodakis Proceedings of the IEEE international conference on computer vision, 1134-1142, 2015 | 1001 | 2015 |
| Dense image registration through MRFs and efficient linear programming B Glocker, N Komodakis, G Tziritas, N Navab, N Paragios Medical image analysis 12 (6), 731-741, 2008 | 570 | 2008 |
| Image completion using efficient belief propagation via priority scheduling and dynamic pruning N Komodakis, G Tziritas IEEE Transactions on Image Processing 16 (11), 2649-2661, 2007 | 486 | 2007 |
| Boosting few-shot visual learning with self-supervision S Gidaris, A Bursuc, N Komodakis, P Pérez, M Cord Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 481 | 2019 |
| Playing with duality: An overview of recent primal? dual approaches for solving large-scale optimization problems N Komodakis, JC Pesquet IEEE Signal Processing Magazine 32 (6), 31-54, 2015 | 481 | 2015 |
| MRF energy minimization and beyond via dual decomposition N Komodakis, N Paragios, G Tziritas IEEE transactions on pattern analysis and machine intelligence 33 (3), 531-552, 2010 | 425 | 2010 |
| MRF optimization via dual decomposition: Message-passing revisited N Komodakis, N Paragios, G Tziritas 2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007 | 406 | 2007 |
| Image completion using global optimization N Komodakis 2006 IEEE Computer Society Conference on Computer Vision and Pattern …, 2006 | 385 | 2006 |
| Building detection in very high resolution multispectral data with deep learning features M Vakalopoulou, K Karantzalos, N Komodakis, N Paragios 2015 IEEE international geoscience and remote sensing symposium (IGARSS …, 2015 | 363 | 2015 |
| Approximate labeling via graph cuts based on linear programming N Komodakis, G Tziritas IEEE transactions on pattern analysis and machine intelligence 29 (8), 1436-1453, 2007 | 331 | 2007 |
| Markov random field modeling, inference & learning in computer vision & image understanding: A survey C Wang, N Komodakis, N Paragios Computer Vision and Image Understanding 117 (11), 1610-1627, 2013 | 326 | 2013 |
| Generating classification weights with gnn denoising autoencoders for few-shot learning S Gidaris, N Komodakis Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 315 | 2019 |
| Rotation equivariant vector field networks D Marcos, M Volpi, N Komodakis, D Tuia Proceedings of the IEEE International Conference on Computer Vision, 5048-5057, 2017 | 306 | 2017 |
