| 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 | 36182 | 2019 |
| End-to-end object detection with transformers N Carion, F Massa, G Synnaeve, N Usunier, A Kirillov, S Zagoruyko European conference on computer vision, 213-229, 2020 | 8851 | 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 | 4537 | 2021 |
| Detectron2 Y Wu, A Kirillov, F Massa, WY Lo, R Girshick | 2423* | 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 | 430* | 2020 |
| Dinov2: Learning robust visual features without supervision M Oquab, T Darcet, T Moutakanni, H Vo, M Szafraniec, V Khalidov, ... arXiv preprint arXiv:2304.07193, 2023 | 291* | 2023 |
| maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch F Massa, R Girshick | 262 | 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 | 115 | 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 | 91* | 2016 |
| 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 | 46 | 2017 |
| Hybrid transformers for music source separation S Rouard, F Massa, A Défossez ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 43 | 2023 |
| xformers: A modular and hackable transformer modelling library B Lefaudeux, F Massa, D Liskovich, W Xiong, V Caggiano, S Naren, M Xu, ... | 40 | 2021 |
| 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 | 26 | 2014 |
| & Chintala S (2019) A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Pytorch: An imperative style, high-performance deep learning library …, 0 | 17 | |
| 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 | 12 | 2016 |
| PyTorch: an imperative style, high-performance deep learning library. arXiv e-prints A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703, 2019 | 9 | 2019 |
| 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 | 6 | 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 | 5 | 2021 |
| Object detection in torch F Massa | 2 | 2016 |
| Relating images and 3D models with convolutional neural networks FVS Massa Université Paris-Est, 2017 | 1 | 2017 |
Adam PaszkeGoogleVerified email at students.mimuw.edu.pl
