Multi-view Tracking Using Weakly Supervised Human Motion Prediction
M. Engilberge, W. Liu, P. Fua
WACV, 2023
Research
My primary research interests are in the fields of Machine Learning, Computer Vision and Natural Language Processing.
From 2017 to 2020, I did a PhD thesis focused on joint visual and textual learning, specifically on Visual-Semantic Embeddings (VSE).
I was advised by Matthieu Cord, Patrick Pérez and Louis Chevallier.
During this time I worked on multimodal representation learning applied to multiple tasks: cross-modal retrieval, phrase grounding, ranking optimization, known instance search and iterative search.
I am excited about the upcoming research challenges, with a keen interest in exploring self-supervised and reasoning methods to bring multimodal interaction to the next level.
Projects
Two-level Data Augmentation for Calibrated Multi-view Detection
M. Engilberge, H. Shi, Z. Wang, P. Fua
WACV, 2023
VideoMem: Constructing, Analyzing, Predicting Short-Term and Long-Term Video Memorability
R. Cohendet, C. Demarty, N. Duong, M. Engilberge
ICCV, 2019
SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates
M. Engilberge, L. Chevallier, P. Pérez, M. Cord
CVPR, 2019
Finding Beans in Burgers: Deep Semantic-Visual Embedding with Localization
M. Engilberge, L. Chevallier, P. Pérez, M. Cord
CVPR, 2018
