Martin Engilberge

Martin Engilberge

Machine learning Researcher for Vision and Language

Research

My primary research interests lie in the fields of Machine Learning, Computer Vision, and Natural Language Processing.

Since 2020, I have been working on diverse topics in machine learning and computer vision. My research encompasses object detection and tracking, domain adaptation/generalization, 3D geometry, and scene understanding.

From 2017 to 2020, I completed my PhD thesis, which focused on joint visual and textual learning, with a specific emphasis on Visual-Semantic Embeddings (VSE). During this period, I was fortunate to be advised by Matthieu Cord, Patrick Pérez, and Louis Chevallier.

My doctoral research centered on multimodal representation learning, applying it to various tasks including cross-modal retrieval, phrase grounding, ranking optimization, known instance search, and iterative search.

Looking ahead, I am enthusiastic about tackling emerging research challenges. I have a particular interest in embodied AI, multimodal interaction, and 3D scene understanding, areas that I believe will play crucial roles in shaping the future of artificial intelligence and computer vision.

Projects

Talks