191 citations · 566 across the 10 of their papers we have counts for
10 papers
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…
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
Onur Tasar, Yuliya Tarabalka, Alain Giros +2
Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain…
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data
Onur Tasar, S L Happy, Yuliya Tarabalka +1
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorat…
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…
Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria
Pierre Alliez, Roberto Di Cosmo, Benjamin Guedj +4
Software is a fundamental pillar of modern scientiic research, not only in computer science, but actually across all elds and disciplines. However, there is a lack of adequate mean…
Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
Onur Tasar, Yuliya Tarabalka, Pierre Alliez
In spite of remarkable success of the convolutional neural networks on semantic segmentation, they suffer from catastrophic forgetting: a significant performance drop for the alrea…
Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement
Kaimo Hu, Dong-Ming Yan, David Bommes +2
The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity r…
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…