Thomas Mensink
Thomas Mensink is a research scientist at Google DeepMind Amsterdam as of October 2026; his research spans computer vision and machine learning. He earned an MSc in artificial intelligence at the University of Amsterdam and a PhD in computer science at Grenoble, then worked as a postdoctoral researcher at UvA and an assistant professor in 3D machine learning. His Fisher-kernel paper received ECCV's Koenderink Award, and his work earned best-paper awards from ACM Multimedia and ICMR. He coauthored the paper introducing Encyclopedic VQA in 2023, a pixel-space diffusion paper in 2025 and a latent-representation paper in 2026.
- Now at
- Google DeepMind
- Works
- 154
- Citations
- 7,952
- h-index
- 30
- Badges
Publication numbers from OpenAlex, as of 22 October 2026.
Timeline
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2010
Improving the Fisher kernel
In 2010, he coauthors “Improving the Fisher Kernel for Large-Scale Image Classification” with Florent Perronnin and Jorge Sánchez at ECCV.
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2012
An AFRIF PhD Thesis Prize
In 2012, his PhD thesis, “Learning Image Classification and Retrieval Models,” wins AFRIF's PhD Thesis Prize.
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By 2013
AI degrees from Amsterdam and Grenoble
At CCVW 2013, he holds an MSc in artificial intelligence from the University of Amsterdam and a PhD in computer science from the University of Grenoble.
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2013
Postdoctoral research in Amsterdam
At CCVW 2013, he is a postdoctoral researcher at the University of Amsterdam, with research interests in machine learning and computer vision.
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2014–2020
Three computer-vision awards
His work receives ACM Multimedia's Best Paper Award in 2014 and ACM ICMR's Best Paper Award in 2016. In 2020, he and coauthors Florent Perronnin and Jorge Sánchez receive ECCV's Koenderink Award for their Fisher Kernel paper.
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2023
Encyclopedic VQA at ICCV
At ICCV 2023, he coauthors the paper introducing Encyclopedic VQA, a dataset of visual questions about detailed properties of fine-grained categories and instances. It contains 221,000 unique question-and-answer pairs, each matched with up to five images.
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2025–2026
Diffusion and latent models
At CVPR 2025, he coauthors “Simpler Diffusion,” a pixel-space diffusion model that achieves 1.5 FID on ImageNet512. In February 2026, he coauthors “Unified Latents,” a framework for learning latent representations that uses a diffusion prior and a diffusion model for decoding.
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As of October 2026
Research at Google DeepMind
He is a research scientist at Google DeepMind Amsterdam after an assistant professorship in 3D machine learning at UvA.
Career
- Since 2025Google DeepMindResearch Scientist
- 2019–2026Google
- 2013–2021University of AmsterdamPostdoctoral researcher
- 2011–2013Xerox
- 2008–2013Inria
Education
- 2013University of AmsterdamMSc in artificial intelligence
- 2013University of GrenoblePhD in computer science
Sources and credits
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- Research
- Invited Speakers - Computer Vision Center
- Thomas Mensink
- Award Calls | ECCV 2020
- Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories
- Simpler Diffusion: 1.5 FID on ImageNet512 with Pixel-space Diffusion
- [2602.17270] Unified Latents (UL): How to train your latents
- Thomas Mensink
- CV Group @ University of Amsterdam




