Search Results for author: Maxim Neumann

Found 9 papers, 5 papers with code

Training general representations for remote sensing using in-domain knowledge

no code implementations30 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.

Representation Learning Transfer Learning

AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification

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.

Classification General Classification +1

In-domain representation learning for remote sensing

1 code implementation15 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)

Multi-Label Image Classification Representation Learning +1

Progressive Neural Architecture Search

16 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.

Evolutionary Algorithms General Classification +3

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