| 4.6 Tasks: Creating Simulated Worlds from Existing Media D Hogg, R Bernardi, D Elliott, R Fernandez, S Frank, M Leordeanu, J Oh, ... Joint Processing of Language and Visual Data for Better Automated …, 0 | | |
| A k-segments algorithm for finding principal curves JJ Verbeek, N Vlassis, B Krose Pattern Recognition Letters 23 (8), 1009-1017, 2002 | 174 | 2002 |
| A robust and efficient video representation for action recognition H Wang, D Oneata, J Verbeek, C Schmid International journal of computer vision (IJCV) 119 (3), 219-238, 2016 | 356 | 2016 |
| A soft k-segments algorithm for principal curves J Verbeek, N Vlassis, B Kröse ICANN, 450-456, 2001 | 43 | 2001 |
| A variational EM algorithm for large-scale mixture modeling J Verbeek, N Vlassis, J Nunnink Annual Conference of the Advanced School for Computing and Imaging, 136-143, 2003 | 29 | 2003 |
| Accelerated EM-based clustering of large data sets JJ Verbeek, JRJ Nunnink, N Vlassis Data Mining and Knowledge Discovery 13, 291-307, 2006 | 67 | 2006 |
| Accelerated greedy mixture learning J Nunnink, J Verbeek, N Vlassis Annual Machine Learning Conference of Belgium and the Netherlands, 2004 | 16* | 2004 |
| Accurate image search using the contextual dissimilarity measure H Jegou, C Schmid, H Harzallah, J Verbeek Transactions on Pattern Analysis and Machine Intelligence (PAMI) 32 (1), 2-11, 2010 | 274 | 2010 |
| Action and event recognition with fisher vectors on a compact feature set D Oneata, J Verbeek, C Schmid International Conference on Computer Vision (ICCV), 2013 | 508 | 2013 |
| Active appearance-based robot localization using stereo vision JM Porta, JJ Verbeek, BJA Kröse Autonomous Robots 18 (1), 103-127, 2005 | 100 | 2005 |
| Adaptative Inference Cost With Convolutional Neural Mixture Models A Ruiz, J Verbeek International Conference on Computer Vision (ICCV), 2019 | 21 | 2019 |
| Adaptive Density Estimation for Generative Models T Lucas, K Shmelkov, K Alahari, C Schmid, J Verbeek Advances in Neural Information Processing Systems (NeurIPS), 2019 | 26 | 2019 |
| Adversarial training of partially invertible variational autoencoders T Lucas, K Shmelkov, K Alahari, C Schmid, J Verbeek Workshop on Invertible Neural Nets and Normalizing Flows (ICML), 2019 | 10* | 2019 |
| An information theoretic approach to finding word groups for text classification J Verbeek University of Amsterdam, 2000 | 9 | 2000 |
| Anytime Inference with Distilled Hierarchical Neural Ensembles A Ruiz, J Verbeek AAAI Conference on Artificial Intelligence (AAAI), 2021 | 27* | 2021 |
| Apprentissage de distance pour l'annotation d'images par plus proches voisins M Guillaumin, J Verbeek, C Schmid, T Mensink Reconnaissance des Formes et Intelligence Artificielle, 2010 | 4 | 2010 |
| Approximate fisher kernels of non-iid image models for image categorization RG Cinbis, J Verbeek, C Schmid Transactions on Pattern Analysis and Machine Intelligence (PAMI) 38 (6 …, 2016 | 36 | 2016 |
| Are Visual Recognition Models Robust to Image Compression? JM Janeiro, S Frolov, A El-Nouby, J Verbeek ICML workshop on Neural Compression: From Information Theory to Applications, 2023 | | 2023 |
| Areas of Attention for Image Captioning M Pedersoli, T Lucas, C Schmid, J Verbeek International Conference on Computer Vision (ICCV), 2017 | 349* | 2017 |
| Automatic face naming with caption-based supervision M Guillaumin, T Mensink, J Verbeek, C Schmid IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008 | 118 | 2008 |
Cordelia SchmidResearch director INRIA 在 inria.fr 的电子邮件经过验证
