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06/30/2023
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning
Magnitude pruning is one of the mainstream methods in lightweight archit...
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05/30/2023
Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning
Graph convolutional networks (GCNs) are nowadays becoming mainstream in ...
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12/28/2022
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection
Satellite image change detection aims at finding occurrences of targeted...
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12/19/2022
Training Lightweight Graph Convolutional Networks with Phase-field Models
In this paper, we design lightweight graph convolutional networks (GCNs)...
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12/09/2022
Frugal Reinforcement-based Active Learning
Most of the existing learning models, particularly deep neural networks,...
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03/25/2022
Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning
Graph convolution networks (GCNs) are currently mainstream in learning w...
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03/22/2022
Reinforcement-based frugal learning for satellite image change detection
In this paper, we introduce a novel interactive satellite image change d...
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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...
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02/25/2022
Extracting Effective Subnetworks with Gumebel-Softmax
Large and performant neural networks are often overparameterized and can...
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12/06/2021
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition
Learning graph convolutional networks (GCNs) is an emerging field which ...
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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...
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10/08/2021
Active learning for interactive satellite image change detection
We introduce in this paper a novel active learning algorithm for satelli...
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07/08/2021
Weight Reparametrization for Budget-Aware Network Pruning
Pruning seeks to design lightweight architectures by removing redundant ...
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04/12/2021
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition
Spectral graph convolutional networks (GCNs) are particular deep models ...
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04/09/2021
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks
Graph convolutional networks (GCNs) aim at extending deep learning to ar...
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12/28/2020
Action Recognition with Kernel-based Graph Convolutional Networks
Learning graph convolutional networks (GCNs) is an emerging field which ...
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12/21/2020
Image Annotation based on Deep Hierarchical Context Networks
Context modeling is one of the most fertile subfields of visual recognit...
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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...
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06/08/2020
Action Recognition with Deep Multiple Aggregation Networks
Most of the current action recognition algorithms are based on deep netw...
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06/08/2020
Deep hierarchical pooling design for cross-granularity action recognition
In this paper, we introduce a novel hierarchical aggregation design that...
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12/29/2019
Deep Context-Aware Kernel Networks
Context plays a crucial role in visual recognition as it provides comple...
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12/12/2019
Totally Deep Support Vector Machines
Support vector machines (SVMs) have been successful in solving many comp...
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10/15/2019
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks
Convolutional neural networks are nowadays witnessing a major success in...
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05/02/2019
Human Action Recognition with Deep Temporal Pyramids
Deep convolutional neural networks (CNNs) are nowadays achieving signifi...
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12/27/2018
Finite State Machines for Semantic Scene Parsing and Segmentation
We introduce in this work a novel stochastic inference process, for scen...
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12/21/2018
Canonical Correlation Analysis for Misaligned Satellite Image Change Detection
Canonical correlation analysis (CCA) is a statistical learning method th...
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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 ...
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04/30/2018
Learning Explicit Deep Representations from Deep Kernel Networks
Deep kernel learning aims at designing nonlinear combinations of multipl...
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03/23/2018
Learning Deep Context-Network Architectures for Image Annotation
Context plays an important role in visual pattern recognition as it prov...
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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 ...
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02/01/2017