Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition
Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial...
Extracting Effective Subnetworks with Gumbel-Softmax
Large and performant neural networks are often overparameterized and can be drastically reduced in size and complexity thanks to pruning. Pruning is a group of methods, which seeks to remove...
FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning
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 from catastrophic...
Active learning for interactive satellite image change detection
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, which asks an...
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks
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 graphs is defined...
Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning
Graph convolution networks (GCNs) are currently mainstream in learning with irregular data. These models rely on message passing and attention mechanisms that capture context and node-to-node...
Reinforcement-based frugal learning for satellite image change detection
In this paper, we introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed approach is iterative and asks the user (oracle) questions about...
Weight Reparametrization for Budget-Aware Network Pruning
Pruning seeks to design lightweight architectures by removing redundant weights in overparameterized networks. Most of the existing techniques first remove structured sub-networks (filters,...
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition
Spectral graph convolutional networks (GCNs) are particular deep models which aim at extending neural networks to arbitrary irregular domains. The principle of these networks consists in...
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks
Convolutional neural networks are nowadays witnessing a major success in different pattern recognition problems. These learning models were basically designed to handle vectorial data such as...
Action Recognition with Kernel-based Graph Convolutional Networks
Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neighborhood...
Image Annotation based on Deep Hierarchical Context Networks
Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic...
Deep Context-Aware Kernel Networks
Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performances of these tasks...
Totally Deep Support Vector Machines
Support vector machines (SVMs) have been successful in solving many computer vision tasks including image and video category recognition especially for small and mid-scale training problems. The...
Stochastic Graphlet Embedding
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives...
Human Action Recognition with Deep Temporal Pyramids
Deep convolutional neural networks (CNNs) are nowadays achieving significant leaps in different pattern recognition tasks including action recognition. Current CNNs are increasingly deeper,...
Canonical Correlation Analysis for Misaligned Satellite Image Change Detection
Canonical correlation analysis (CCA) is a statistical learning method that seeks to build view-independent latent representations from multi-view data. This method has been successfully applied to...