Etienne Meunier

Orcid: 0000-0002-5310-2305

According to our database1, Etienne Meunier authored at least 13 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach.
CoRR, March, 2026

Jacobian Regularization Stabilizes Long-Term Integration of Neural Differential Equations.
CoRR, February, 2026

Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting.
CoRR, February, 2026

Segmenting the Motion Components of a Video: A Long-Term Unsupervised Model.
IEEE Trans. Pattern Anal. Mach. Intell., January, 2026

2025
Towards fully differentiable neural ocean model with Veros.
CoRR, November, 2025

Learning to generate physical ocean states: Towards hybrid climate modeling.
CoRR, February, 2025

2024
Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
ADA-SHARK: A Shark Detection Framework Employing Underwater Cameras and Domain Adversarial Neural Nets.
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., December, 2023

EM-Driven Unsupervised Learning for Efficient Motion Segmentation.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

Unsupervised learning for motion segmentation and motion saliency in videos. (Apprentissage non supervisé pour la segmentation et la saillance du mouvement dans des vidéos).
PhD thesis, 2023

Unsupervised motion segmentation in one go: Smooth long-term model over a video.
CoRR, 2023

Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2021
Unsupervised computation of salient motion maps from the interpretation of a frame-based classification network.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021


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