no code implementations • 22 Sep 2022 • Zhaoyuan Yang, Yewteck Tan, Shiraj Sen, Johan Reimann, John Karigiannis, Mohammed Yousefhussien, Nurali Virani
We test the hypothesis that model trained on a single dataset may not generalize to other off-road navigation datasets and new locations due to the input distribution drift.
no code implementations • 21 May 2020 • Mohammed Yousefhussien, David J. Kelbe, Carl Salvaggio
When 3D-point clouds from overhead sensors are used as input to remote sensing data exploitation pipelines, a large amount of effort is devoted to data preparation.
no code implementations • 3 Oct 2017 • Mohammed Yousefhussien, David J. Kelbe, Emmett J. Ientilucci, Carl Salvaggio
In this paper we present a 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data, if available, to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion.
no code implementations • WS 2017 • Kushal Kafle, Mohammed Yousefhussien, Christopher Kanan
Data augmentation is widely used to train deep neural networks for image classification tasks.