Meunier Etienne

Research

My current work focuses on computer vision, machine learning, video analysis and unsupervised learning.

	Unsupervised motion segmentation in one go: Smooth long-term model over a video Etienne Meunier and Patrick Bouthemy ArXiv, 2023 arxiv / We learn to perform motion segmentation over a video sequence in a single pass in a fully unsupervised manner, using a long-term spatio-temporal model based on splines. The method is very fast at test time and provides temporally consistent labels.
	Unsupervised space-time network for temporally-consistent segmentation of multiple motions Etienne Meunier and Patrick Bouthemy Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 paper / video / code / We extend unsupervised motion segmentation to longer sequences by leveraging spatio-temporal motion models and 3D networks. Provide consistent labels accros the video and improves network robustness to erroneous optical flow.
	EM-driven unsupervised learning for motion segmentation Etienne Meunier, Anaïs Badoual and Patrick Bouthemy IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2023 paper / arxiv / code / We introduce a loss function theoretically grounded in the expectation maximisation framework to train networks to segment optical flow fields without any ground truth labels. Easy to set-up, robust to errors and computationally cheap, it can also be used as a regularisation term for any video segmentation project.
	Unsupervised computation of salient motion maps from the interpretation of a frame- based classification network Etienne Meunier and Patrick Bouthemy British Machine Vision Conference (BMVC) , 2021 paper / video / We detect motion saliency in videos using a weakly-supervised approach based on the gradient-based interpretation of a classification network. Applications on handheld camera videos to distinguish parallax from independent motion and on crowd videos for anomaly motion detection.
	Interpreting deep learning models in genomics using genetic algorithm Etienne Meunier, Gherman Novakovsky and Sara Mostafavi Machine Learning in Computational Biology 2019 (MLCB), 2019 We developed a genetic algorithm to generate diverse DNA sequences that maximize the CNN model’s output, which predicts the cell type specificity of accessible DNA regions. Our tool provide a fast way to extract motifs specific to a given cell type and the results show biological relevance.

Other Projects

These include coursework, side projects, and research work that has not been published.

Shark detection

Consulting
2019-10-01

We designed a functional shark detection pipeline that combines deep learning techniques for underwater image-based shark detection and an adversarial framework for domain adaptation. The pipeline incorporates a network of real-time underwater cameras, enabling efficient shark detection and immediate communication of alerts to on-ground sites. The pipeline is undergoing testing by the CSR in La Reunion Island.

Micropolis GAN

BU Advanced Machine Learning and Neural Networks
2019-09-01
code /

We trained a GAN to generate city structures within the virtual environment of the game “Micropolis.” Our primary objective was to evaluate the generator’s ability to produce city layouts that adhere to the game’s specific requirements. To gauge the effectiveness of the generator, we compared the cities it generated with those obtained through a genetic algorithm that was guided by the game’s scoring function.

Etienne Meunier

Research

Unsupervised motion segmentation in one go: Smooth long-term model over a video

Unsupervised space-time network for temporally-consistent segmentation of multiple motions

EM-driven unsupervised learning for motion segmentation

Unsupervised computation of salient motion maps from the interpretation of a frame- based classification network

Interpreting deep learning models in genomics using genetic algorithm

Other Projects

Shark detection

Micropolis GAN