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valentin gabeur valentin gabeur ai researcher at google deepmind gemini specializing in multi modal learning postdoc at meta ai sam 2 phd from inria msc from toulouse university msc from icam former mechanical engineer with 6 years of experience in industrial automation email nbsp nbsp cv nbsp nbsp google scholar nbsp nbsp linkedin nbsp nbsp twitter nbsp nbsp github research publications sam 2 segment anything in images and videos nikhila ravi valentin gabeur yuan ting hu ronghang hu chaitanya ryali tengyu ma haitham khedr roman rädle chloe rolland laura gustafson eric mintun junting pan kalyan vasudev alwala nicolas carion chao yuan wu ross girshick piotr dollár christoph feichtenhofer arxiv 2024 nbsp paper demo project page blog dataset github promptable visual segmentation in images and videos avatar unconstrained audiovisual speech recognition valentin gabeur paul hongsuck seo arsha nagrani chen sun karteek alahari cordelia schmid interspeech 2022 nbsp 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 nbsp 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 nbsp 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 nbsp 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 nbsp arxiv bibtex efficient 3d shape representation through the combination of depth maps credits to jon barron for the template
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