20 citations · 40 across the 46 of their papers we have counts for
6 papers · 1 filter
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition
Hichem Sahbi
Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operatin…
FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning
Hichem Sahbi, Haoming Zhan
Deep neural networks (DNNs) have recently achieved a great success in computer vision and several related fields. Despite such progress, current neural architectures still suffer f…
Active learning for interactive satellite image change detection
Hichem Sahbi, Sebastien Deschamps, Andrei Stoian
We introduce in this paper a novel active learning algorithm for satellite image change detection. The proposed solution is interactive and based on a question and answer model, wh…
Weight Reparametrization for Budget-Aware Network Pruning
Robin Dupont, Hichem Sahbi, Guillaume Michel
Pruning seeks to design lightweight architectures by removing redundant weights in overparameterized networks. Most of the existing techniques first remove structured sub-networks…
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition
Hichem Sahbi
Spectral graph convolutional networks (GCNs) are particular deep models which aim at extending neural networks to arbitrary irregular domains. The principle of these networks consi…
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks
Hichem Sahbi
Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, namely graphs. Their success is highly dependent on how the topology of input gra…