Lionel Pibre

According to our database, Lionel Pibre authored at least 9 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Boxes to Segmentation to Boxes Again for a Better Detection.

Théo Oriol, Lionel Pibre, Jérôme Pasquet, Zamirddine Mari

Proceedings of the Thirteenth IEEE International Conference on Image Processing Theory, 2024

2023

Audio-video fusion strategies for active speaker detection in meetings.

Lionel Pibre, Francisco Madrigal, Cyrille Equoy, Frédéric Lerasle, Thomas Pellegrini, Julien Pinquier, Isabelle Ferrané

Multim. Tools Appl., April, 2023

2022

New Active Learning Approach for Seabed Segmentation.

Lionel Pibre, Jérôme Pasquet, Vincent Douzal, Claire Noël

Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021

Automatic macro segmentation into interaction sequence: a silence-based approach for meeting structuring.

Lionel Pibre, Sélim Mechrouh, Thomas Pellegrini, Julien Pinquier, Isabelle Ferrané

Proceedings of the 18th International Conference on Content-Based Multimedia Indexing, 2021

2020

Audio-Video detection of the active speaker in meetings.

Francisco Madrigal, Frédéric Lerasle, Lionel Pibre, Isabelle Ferrané

Proceedings of the 25th International Conference on Pattern Recognition, 2020

2018

Localisation d'objets urbains à partir de sources multiples dont des images aériennes. (Localization of urban objects from multiple sources, including aerial imagery).

Lionel Pibre

PhD thesis, 2018

2017

How to deal with multi-source data for tree detection based on deep learning.

Lionel Pibre, Marc Chaumont, Gérard Subsol, Dino Ienco, Mustapha Derras

Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

2016

Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch.

Lionel Pibre, Jérôme Pasquet, Dino Ienco, Marc Chaumont

Proceedings of the Media Watermarking, 2016

2015

Deep Learning for steganalysis is better than a Rich Model with an Ensemble Classifier, and is natively robust to the cover source-mismatch.

Lionel Pibre, Jérôme Pasquet, Dino Ienco, Marc Chaumont

CoRR, 2015