| A comprehensive analysis of deep regression S Lathuilière, P Mesejo, X Alameda-Pineda, R Horaud IEEE transactions on pattern analysis and machine intelligence 42 (9), 2065-2081, 2019 | 308 | 2019 |
| Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions S Tulyakov, X Alameda-Pineda, E Ricci, L Yin, JF Cohn, N Sebe Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 299 | 2016 |
| How to train your deep multi-object tracker Y Xu, A Osep, Y Ban, R Horaud, L Leal-Taixé, X Alameda-Pineda Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 210* | 2020 |
| SALSA: A Novel Dataset for Multimodal Group Behaviour Analysis X Alameda-Pineda, J Staiano, R Subramanian, L Batrinca, E Ricci, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (8), 1707-1720, 2016 | 145 | 2016 |
| EM algorithms for weighted-data clustering with application to audio-visual scene analysis ID Gebru, X Alameda-Pineda, F Forbes, R Horaud IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 | 120 | 2016 |
| A geometric approach to sound source localization from time-delay estimates X Alameda-Pineda, R Horaud IEEE/ACM Transactions on Audio, Speech, and Language Processing 22 (6), 1082 …, 2014 | 108 | 2014 |
| Recognizing emotions from abstract paintings using non-linear matrix completion X Alameda-Pineda, E Ricci, Y Yan, N Sebe Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 106 | 2016 |
| Dynamical variational autoencoders: A comprehensive review L Girin, S Leglaive, X Bie, J Diard, T Hueber, X Alameda-Pineda Foundations and Trends in Machine Learning 15 (1-2), 1-175, 2020 | 102 | 2020 |
| Learning deep structured multi-scale features using attention-gated crfs for contour prediction D Xu, W Ouyang, X Alameda-Pineda, E Ricci, X Wang, N Sebe Advances in neural information processing systems 30, 2017 | 100 | 2017 |
| Analyzing free-standing conversational groups: A multimodal approach X Alameda-Pineda, Y Yan, E Ricci, O Lanz, N Sebe Proceedings of the 23rd ACM international conference on Multimedia, 5-14, 2015 | 99 | 2015 |
| Transcenter: Transformers with dense queries for multiple-object tracking Y Xu, Y Ban, G Delorme, C Gan, D Rus, X Alameda-Pineda IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 94 | 2022 |
| Tracking multiple persons based on a variational bayesian model Y Ban, S Ba, X Alameda-Pineda, R Horaud Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 80 | 2016 |
| A variational EM algorithm for the separation of time-varying convolutive audio mixtures D Kounades-Bastian, L Girin, X Alameda-Pineda, S Gannot, R Horaud IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (8), 1408 …, 2016 | 61 | 2016 |
| Every Smile is Unique: Landmark-Guided Diverse Smile Generation W Wei, X Alameda-Pineda, D Xu, E Ricci, P Fua, N Sebe IEEE/CVF International Conference on Computer Vision and Pattern Recognition, 2018 | 59* | 2018 |
| Audio-visual speech enhancement using conditional variational auto-encoders M Sadeghi, S Leglaive, X Alameda-Pineda, L Girin, R Horaud IEEE/ACM Transactions on Audio, Speech, and Language Processing 28, 1788-1800, 2020 | 58 | 2020 |
| A recurrent variational autoencoder for speech enhancement S Leglaive, X Alameda-Pineda, L Girin, R Horaud ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 56 | 2020 |
| Variational bayesian inference for audio-visual tracking of multiple speakers Y Ban, X Alameda-Pineda, L Girin, R Horaud IEEE transactions on pattern analysis and machine intelligence 43 (5), 1761-1776, 2019 | 48 | 2019 |
| An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes S Ba, X Alameda-Pineda, A Xompero, R Horaud Computer Vision and Image Understanding, 2016 | 47 | 2016 |
| Sound representation and classification benchmark for domestic robots J Maxime, X Alameda-Pineda, L Girin, R Horaud 2014 IEEE International Conference on Robotics and Automation (ICRA), 6285-6292, 2014 | 46 | 2014 |
| Self-supervised models are continual learners E Fini, VGT Da Costa, X Alameda-Pineda, E Ricci, K Alahari, J Mairal Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 44 | 2022 |
Radu Patrice HoraudEmeritus (since October 2022) at Inria Grenoble在 inria.fr 的电子邮件经过验证
