| 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 | 41628 | 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 | 11087 | 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 | 5596 | 2021 |
| Detectron2 Y Wu, A Kirillov, F Massa, WY Lo, R Girshick | 2752 | 2019 |
| 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 | 826* | 2023 |
| Pytorch: An imperative style, high-performance deep learning library B Steiner, Z DeVito, S Chintala, S Gross, A Paske, F Massa, A Lerer, ... | 751* | 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 | 501* | 2020 |
| maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch F Massa, R Girshick | 273 | 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 | 117 | 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 | 94* | 2016 |
| xformers: A modular and hackable transformer modelling library B Lefaudeux, F Massa, D Liskovich, W Xiong, V Caggiano, S Naren, M Xu, ... | 69 | 2022 |
| Hybrid transformers for music source separation S Rouard, F Massa, A Défossez ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 68 | 2023 |
| 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 | 49 | 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 | 24 | 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 | 9 | 2021 |
| Flash-decoding for long-context inference T Dao, D Haziza, F Massa, G Sizov | 7 | 2023 |
| Torchvision F Massa, S Chintala torchvision, 2017 | 6* | 2017 |
| 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 |
| Relating images and 3D models with convolutional neural networks FVS Massa Université Paris-Est, 2017 | 2 | 2017 |
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