22 citations · 24 across the 4 of their papers we have counts for
5 papers
DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
Oriel Frigo, Lucien Martin-Gaffé, Catherine Wacongne
In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a…
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
Oriel Frigo, Rémy Brossard, David Dehaene
We propose the Graph Context Encoder (GCE), a simple but efficient approach for graph representation learning based on graph feature masking and reconstruction. GCE models are trai…
Realistic molecule optimization on a learned graph manifold
Rémy Brossard, Oriel Frigo, David Dehaene
Deep learning based molecular graph generation and optimization has recently been attracting attention due to its great potential for de novo drug design. On the one hand, recent m…
Graph convolutions that can finally model local structure
Rémy Brossard, Oriel Frigo, David Dehaene
Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles. This hin…
Iterative energy-based projection on a normal data manifold for anomaly localization
David Dehaene, Oriel Frigo, Sébastien Combrexelle +1
Autoencoder reconstructions are widely used for the task of unsupervised anomaly localization. Indeed, an autoencoder trained on normal data is expected to only be able to reconstr…