191 citations · 566 across the 10 of their papers we have counts for
5 papers · 1 filter
BPNet: Bézier Primitive Segmentation on 3D Point Clouds
Rao Fu, Cheng Wen, Qian Li +2
This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. The existing works treat different primitive types…
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
Onur Tasar, Alain Giros, Yuliya Tarabalka +2
The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting la…
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks
Onur Tasar, S L Happy, Yuliya Tarabalka +1
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite…
High-Resolution Semantic Labeling with Convolutional Neural Networks
Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat +1
Convolutional neural networks (CNNs) have received increasing attention over the last few years. They were initially conceived for image categorization, i.e., the problem of assign…
Recurrent Neural Networks to Correct Satellite Image Classification Maps
Emmanuel Maggiori, Guillaume Charpiat, Yuliya Tarabalka +1
While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the prope…