| Pytorch: An imperative style, high-performance deep learning library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Advances in neural information processing systems 32, 2019 | 26225 | 2019 |
| End-to-end object detection with transformers N Carion, F Massa, G Synnaeve, N Usunier, A Kirillov, S Zagoruyko Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 5438 | 2020 |
| Training data-efficient image transformers & distillation through attention H Touvron, M Cord, M Douze, F Massa, A Sablayrolles, H Jégou International conference on machine learning, 10347-10357, 2021 | 2763 | 2021 |
| Detectron2 Y Wu, A Kirillov, F Massa, WY Lo, R Girshick | 1830* | 2019 |
| Mlperf inference benchmark VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 323* | 2020 |
| maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch F Massa, R Girshick | 252 | 2018 |
| Deep exemplar 2d-3d detection by adapting from real to rendered views F Massa, BC Russell, M Aubry Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 109 | 2016 |
| Crafting a multi-task cnn for viewpoint estimation M Francisco, M Renaud, A Mathieu Proceedings of the British Machine Vision Conference, 91.1-91.12, 2016 | 85* | 2016 |
| PyTorch: An Imperative Style A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... High-performance deep learning library 12, 1912 | 65 | 1912 |
| Frame interpolation with multi-scale deep loss functions and generative adversarial networks J van Amersfoort, W Shi, A Acosta, F Massa, J Totz, Z Wang, J Caballero arXiv preprint arXiv:1711.06045, 2017 | 44 | 2017 |
| Convolutional neural networks for joint object detection and pose estimation: A comparative study F Massa, M Aubry, R Marlet arXiv preprint arXiv:1412.7190, 2014 | 25 | 2014 |
| Automatic 3d car model alignment for mixed image-based rendering R Ortiz-Cayon, A Djelouah, F Massa, M Aubry, G Drettakis 2016 Fourth International Conference on 3D Vision (3DV), 286-295, 2016 | 11 | 2016 |
| The vision behind mlperf: Understanding ai inference performance VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu IEEE Micro 41 (3), 10-18, 2021 | 5 | 2021 |
| xFormers: A modular and hackable Transformer modelling library B Lefaudeux, F Massa, D Liskovich, W Xiong, V Caggiano, S Naren, M Xu, ... | 5 | 2021 |
| Frame interpolation with multi-scale deep loss functions and generative adversarial networks J Van Amersfoort, W Shi, J Caballero, AAA Diaz, F Massa, J Totz, Z Wang US Patent 11,122,238, 2021 | 4 | 2021 |
| Object detection in torch F Massa | 2 | 2016 |
| Hybrid Transformers for Music Source Separation S Rouard, F Massa, A Défossez arXiv preprint arXiv:2211.08553, 2022 | 1 | 2022 |
| Relating images and 3D models with convolutional neural networks FVS Massa Université Paris-Est, 2017 | 1 | 2017 |
| Mise en relation d'images et de modèles 3D avec des réseaux de neurones convolutifs FV Suzano Massa Paris Est, 2017 | | 2017 |
| Relating images and 3D models with convolutional neural networks.(Mise en relation d'images et de modèles 3D avec des réseaux de neurones convolutifs). FVS Massa University of Paris-Est, France, 2017 | | 2017 |
Adam PaszkeGoogle在 students.mimuw.edu.pl 的电子邮件经过验证
