9 papers
Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition
Hichem Sahbi
Magnitude Pruning is a staple lightweight network design method which seeks to remove connections with the smallest magnitude. This process is either achieved in a structured or un…
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition
Hichem Sahbi
Deep neural networks (DNNs) are nowadays witnessing a major success in solving many pattern recognition tasks including skeleton-based classification. The deployment of DNNs on edg…
Few-Shot Object Detection with Sparse Context Transformers
Jie Mei, Mingyuan Jiu, Hichem Sahbi +2
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is tran…
One-Shot Multi-Rate Pruning of Graph Convolutional Networks
Hichem Sahbi
In this paper, we devise a novel lightweight Graph Convolutional Network (GCN) design dubbed as Multi-Rate Magnitude Pruning (MRMP) that jointly trains network topology and weights…
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection
Hichem Sahbi
We introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed method is iterative and consists in frugally probing the user (or…
MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty
Rémi Marsal, Florian Chabot, Angelique Loesch +2
Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfe…