b'Hichem Sahbi'

research

∙ 06/30/2023

Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning

Magnitude pruning is one of the mainstream methods in lightweight archit...

0 Hichem Sahbi, et al. ∙

research

∙ 05/30/2023

Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning

Graph convolutional networks (GCNs) are nowadays becoming mainstream in ...

0 Hichem Sahbi, et al. ∙

research

∙ 12/28/2022

Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection

Satellite image change detection aims at finding occurrences of targeted...

0 Hichem Sahbi, et al. ∙

research

∙ 12/19/2022

Training Lightweight Graph Convolutional Networks with Phase-field Models

In this paper, we design lightweight graph convolutional networks (GCNs)...

0 Hichem Sahbi, et al. ∙

research

∙ 12/09/2022

Frugal Reinforcement-based Active Learning

Most of the existing learning models, particularly deep neural networks,...

0 Sebastien Deschamps, et al. ∙

research

∙ 03/25/2022

Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning

Graph convolution networks (GCNs) are currently mainstream in learning w...

0 Hichem Sahbi, et al. ∙

research

∙ 03/22/2022

Reinforcement-based frugal learning for satellite image change detection

In this paper, we introduce a novel interactive satellite image change d...

0 Sebastien Deschamps, et al. ∙

research

∙ 03/22/2022

Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection

In this paper, we devise a novel interactive satellite image change dete...

0 Hichem Sahbi, et al. ∙

research

∙ 02/25/2022

Extracting Effective Subnetworks with Gumebel-Softmax

Large and performant neural networks are often overparameterized and can...

0 Robin Dupont, et al. ∙

research

∙ 12/06/2021

Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition

Learning graph convolutional networks (GCNs) is an emerging field which ...

0 Hichem Sahbi, et al. ∙

research

∙ 11/22/2021

FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning

Deep neural networks (DNNs) have recently achieved a great success in co...

0 Hichem Sahbi, et al. ∙

research

∙ 10/08/2021

Active learning for interactive satellite image change detection

We introduce in this paper a novel active learning algorithm for satelli...

0 Hichem Sahbi, et al. ∙

research

∙ 07/08/2021

Weight Reparametrization for Budget-Aware Network Pruning

Pruning seeks to design lightweight architectures by removing redundant ...

0 Robin Dupont, et al. ∙

research

∙ 04/12/2021

Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition

Spectral graph convolutional networks (GCNs) are particular deep models ...

0 Hichem Sahbi, et al. ∙

research

∙ 04/09/2021

Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks

Graph convolutional networks (GCNs) aim at extending deep learning to ar...

0 Hichem Sahbi, et al. ∙

research

∙ 12/28/2020

Action Recognition with Kernel-based Graph Convolutional Networks

Learning graph convolutional networks (GCNs) is an emerging field which ...

0 Hichem Sahbi, et al. ∙

research

∙ 12/21/2020

Image Annotation based on Deep Hierarchical Context Networks

Context modeling is one of the most fertile subfields of visual recognit...

0 Mingyuan Jiu, et al. ∙

research

∙ 06/26/2020

End-to-end training of deep kernel map networks for image classification

Deep kernel map networks have shown excellent performances in various cl...

0 Mingyuan Jiu, et al. ∙

research

∙ 06/08/2020

Action Recognition with Deep Multiple Aggregation Networks

Most of the current action recognition algorithms are based on deep netw...

0 Ahmed Mazari, et al. ∙

research

∙ 06/08/2020

Deep hierarchical pooling design for cross-granularity action recognition

In this paper, we introduce a novel hierarchical aggregation design that...

0 Ahmed Mazari, et al. ∙

research

∙ 12/29/2019

Deep Context-Aware Kernel Networks

Context plays a crucial role in visual recognition as it provides comple...

15 Mingyuan Jiu, et al. ∙

research

∙ 12/12/2019

Totally Deep Support Vector Machines

Support vector machines (SVMs) have been successful in solving many comp...

0 Hichem Sahbi, et al. ∙

research

∙ 10/15/2019

Human Action Recognition with Multi-Laplacian Graph Convolutional Networks

Convolutional neural networks are nowadays witnessing a major success in...

0 Ahmed Mazari, et al. ∙

research

∙ 05/02/2019

Human Action Recognition with Deep Temporal Pyramids

Deep convolutional neural networks (CNNs) are nowadays achieving signifi...

0 Ahmed Mazari, et al. ∙

research

∙ 12/27/2018

Finite State Machines for Semantic Scene Parsing and Segmentation

We introduce in this work a novel stochastic inference process, for scen...

0 Hichem Sahbi, et al. ∙

research

∙ 12/21/2018

Canonical Correlation Analysis for Misaligned Satellite Image Change Detection

Canonical correlation analysis (CCA) is a statistical learning method th...

0 Hichem Sahbi, et al. ∙

research

∙ 12/21/2018

Cascaded Coarse-to-Fine Deep Kernel Networks for Efficient Satellite Image Change Detection

Deep networks are nowadays becoming popular in many computer vision and ...

0 Hichem Sahbi, et al. ∙

research

∙ 04/30/2018

Learning Explicit Deep Representations from Deep Kernel Networks

Deep kernel learning aims at designing nonlinear combinations of multipl...

0 Mingyuan Jiu, et al. ∙

research

∙ 03/23/2018

Learning Deep Context-Network Architectures for Image Annotation

Context plays an important role in visual pattern recognition as it prov...

0 Mingyuan Jiu, et al. ∙

research

∙ 02/28/2018

Graph Kernels based on High Order Graphlet Parsing and Hashing

Graph-based methods are known to be successful in many machine learning ...

0 Anjan Dutta, et al. ∙

research

∙ 02/01/2017

High Order Stochastic Graphlet Embedding for Graph-Based Pattern Recognition

Graph-based methods are known to be successful for pattern description a...

0 Anjan Dutta, et al. ∙

Hichem Sahbi

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