David Filliat
David Filliat
Professor, ENSTA Paris
Verified email at - Homepage
Cited by
Cited by
Fast and incremental method for loop-closure detection using bags of visual words
A Angeli, D Filliat, S Doncieux, JA Meyer
IEEE transactions on robotics 24 (5), 1027-1037, 2008
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
T Lesort, V Lomonaco, A Stoian, D Maltoni, D Filliat, N Díaz-Rodríguez
Information fusion 58, 52-68, 2020
State representation learning for control: An overview
T Lesort, N Díaz-Rodríguez, JF Goudou, D Filliat
Neural Networks 108, 379-392, 2018
Map-based navigation in mobile robots: I. a review of localization strategies
D Filliat, JA Meyer
Cognitive systems research 4 (4), 243-282, 2003
A visual bag of words method for interactive qualitative localization and mapping
D Filliat
Proceedings 2007 IEEE International Conference on Robotics and Automation …, 2007
Map-based navigation in mobile robots:: II. a review of map-learning and path-planning strategies
JA Meyer, D Filliat
Cognitive Systems Research 4 (4), 283-317, 2003
Explainable artificial intelligence (xai) on timeseries data: A survey
T Rojat, R Puget, D Filliat, J Del Ser, R Gelin, N Díaz-Rodríguez
arXiv preprint arXiv:2104.00950, 2021
Don't forget, there is more than forgetting: new metrics for Continual Learning
N Díaz-Rodríguez, V Lomonaco, D Filliat, D Maltoni
NeurIPS workshop on Continual Learning 2018 arXiv preprint arXiv:1810.13166, 2018
Asynchronous event-based multikernel algorithm for high-speed visual features tracking
X Lagorce, C Meyer, SH Ieng, D Filliat, R Benosman
IEEE transactions on neural networks and learning systems 26 (8), 1710-1720, 2014
Generative models from the perspective of continual learning
T Lesort, H Caselles-Dupré, M Garcia-Ortiz, A Stoian, D Filliat
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
Real-time visual loop-closure detection
A Angeli, S Doncieux, JA Meyer, D Filliat
2008 IEEE international conference on robotics and automation, 1842-1847, 2008
Ontology-based context awareness for driving assistance systems
A Armand, D Filliat, J Ibañez-Guzman
2014 IEEE intelligent vehicles symposium proceedings, 227-233, 2014
Visual topological SLAM and global localization
A Angeli, S Doncieux, JA Meyer, D Filliat
2009 IEEE International Conference on Robotics and Automation, 4300-4305, 2009
Shrec'17 track: 3d hand gesture recognition using a depth and skeletal dataset
Q De Smedt, H Wannous, JP Vandeborre, J Guerry, B Le Saux, D Filliat
3DOR-10th Eurographics Workshop on 3D Object Retrieval, 1-6, 2017
Incremental vision-based topological SLAM
A Angeli, S Doncieux, JA Meyer, D Filliat
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2008
Discorl: Continual reinforcement learning via policy distillation
R Traoré, H Caselles-Dupré, T Lesort, T Sun, G Cai, N Díaz-Rodríguez, ...
NeurIPS workshop on Deep Reinforcement Learning,, 2019
Snapnet-r: Consistent 3d multi-view semantic labeling for robotics
J Guerry, A Boulch, B Le Saux, J Moras, A Plyer, D Filliat
Proceedings of the IEEE international conference on computer vision …, 2017
Environment exploration for object-based visual saliency learning
C Craye, D Filliat, JF Goudou
2016 IEEE international conference on robotics and automation (ICRA), 2303-2309, 2016
Object learning through active exploration
S Ivaldi, N Lyubova, A Droniou, V Padois, D Filliat, PY Oudeyer, O Sigaud
IEEE Transactions on Autonomous Mental Development 6 (1), 56-72, 2013
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
N Díaz-Rodríguez, A Lamas, J Sanchez, G Franchi, I Donadello, S Tabik, ...
Information Fusion 79, 58-83, 2021
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