Valentin Gabeur

I am a postdoctoral researcher at FAIR, Meta AI. My research focuses on multi-modal learning for video understanding, at the intersection of computer vision, audio processing, speech recognition and natural language understanding.

I completed my PhD in October 2022 from Inria and Grenoble-Alpes University, where I worked in the Thoth team on multi-modal learning, advised by Cordelia Schmid and Karteek Alahari. During that time, I also worked as a Student Researcher at Google AI Research. I received a MS in Robotics from Toulouse III University in 2018.

Prior to that, I worked for 6 years on industrial automation and machine design in China, France and the USA, mostly as a mechanical engineer. I received a MS in Engineering from ICAM Lille in 2011.

Email / CV / Google Scholar / LinkedIn / Twitter / GitHub

Research publications:

	AVATAR: Unconstrained Audiovisual Speech Recognition Valentin Gabeur, Paul Hongsuck Seo, Arsha Nagrani, Chen Sun, Karteek Alahari, Cordelia Schmid INTERSPEECH, 2022* arXiv / project page / bibtex Leveraging the full frame visual context to improve speech recognition in videos.
	Masking Modalities for Cross-modal Video Retrieval Valentin Gabeur, Arsha Nagrani, Chen Sun, Karteek Alahari, Cordelia Schmid WACV, 2022 arXiv / bibtex Pre-training strategy for learning multi-modal fusion from unlabelled videos.
	Multi-modal Transformer for Video Retrieval Valentin Gabeur, Chen Sun, Karteek Alahari, Cordelia Schmid ECCV, 2020 (Spotlight paper) arXiv / code, models, data / bibtex Cross-modal architecture to encode language captions and videos in a common embedding space.
	CVPR 2020 Video Pentathlon Challenge: Multi-modal Transformer for Video Retrieval Valentin Gabeur, Chen Sun, Karteek Alahari, Cordelia Schmid CVPR Video Pentathlon Workshop, 2020 (First place) report / paper / challenge / recording Winning approach for the CVPR 2020 Video Pentathlon Challenge, a video retrieval competition.
	Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images Valentin Gabeur, Jean-Sebastien Franco, Xavier Martin, Cordelia Schmid, Gregory Rogez ICCV, 2019 arXiv / bibtex Efficient 3D shape representation through the combination of depth maps.

Credits to Jon Barron for the template.