SCR: Smooth Contour Regression with Geometric Priors

Autor: Bahl, Gaetan, Daniel, Lionel, Lafarge, Florent

While object detection methods traditionally make use of pixel-level masks or bounding boxes, alternative representations such as polygons or active contours have recently emerged. Among them, methods based on the regression of Fourier or Chebyshev c

Externí odkaz: http://arxiv.org/abs/2202.03784

Binary Graph Neural Networks

Autor: Bahri, Mehdi, Bahl, Gaétan, Zafeiriou, Stefanos

Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementa

Externí odkaz: http://arxiv.org/abs/2012.15823

Scanner Neural Network for On-Board Segmentation of Satellite Images

Autor: Lafarge, Florent, Bahl, Gaétan

Publikováno v: IGARSS 2022 – IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2022 – IEEE International Geoscience and Remote Sensing Symposium, Jul 2022, Kuala Lumpur, Malaysia

International audience; Traditional Convolutional Neural Networks (CNN) for semantic segmentation of images use 2D convolution operations. While the spatial inductive bias of 2D convolutions allow CNNs to build hierarchical feature representations, t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba39a7d044534dc8c6a8b43f42ac231e
https://doi.org/10.1109/igarss46834.2022.9884523

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