18 code implementations • ECCV 2018 • Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #15 on Neural Architecture Search on NAS-Bench-201, ImageNet-16-120 (Accuracy (Val) metric)
no code implementations • 25 Sep 2019 • Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby
Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.
2 code implementations • arXiv 2020 • Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby
And, how close are we to general visual representations?
Ranked #10 on Image Classification on VTAB-1k (using extra training data)
1 code implementation • 15 Nov 2019 • Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil Houlsby
Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community.
Ranked #1 on Multi-Label Image Classification on BigEarthNet (mAP (macro) metric)
no code implementations • ECCV 2020 • Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, Wei Hua
The discovered attention cells can be seamlessly inserted into existing backbone networks, e. g., I3D or S3D, and improve video classification accuracy by more than 2% on both Kinetics-600 and MiT datasets.
no code implementations • 30 Sep 2020 • Maxim Neumann, André Susano Pinto, Xiaohua Zhai, Neil Houlsby
Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples.
1 code implementation • NeurIPS 2021 • Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby
We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks.
Ranked #1 on Few-Shot Image Classification on ImageNet - 5-shot
no code implementations • 26 Jul 2021 • Wojciech Sirko, Sergii Kashubin, Marvin Ritter, Abigail Annkah, Yasser Salah Eddine Bouchareb, Yann Dauphin, Daniel Keysers, Maxim Neumann, Moustapha Cisse, John Quinn
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes.
2 code implementations • 12 May 2022 • Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, Neil Houlsby
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
Ranked #1 on One-Shot Object Detection on MS COCO