Pascal Mettes
Assistant Professor - University of Amsterdam
Computer vision
Hierarchical knowledge
Hyperbolic geometry
Me and my team focus on deep learning in hyperbolic space. Within deep learning, we tend to question all aspects of training neural networks, from architectures and optimization to data and tricks. The most fundamental assumption, namely to operate in Euclidean space, is however rarely questioned. We believe that opening our scope beyond Euclid opens an entirely new worlds for deep learning. Specifically, we focus on hyperbolic deep learning. Learning in hyperbolic space enables us to learn hierarchical representations, with stronger robustness (with respect to OOD and adversarial samples), more compactness, and with the possibility of incorporating prior knowledge. We also consider it the native space for vision-language models. In our lab, we are therefore spearheading hyperbolic deep learning through algorithmic advances, open-source developments, and international workshops, tutorials, and talks.
This page will highlight some success stories of our journey, including the first software library for hyperbolic learning, the first international tutorials and survey on hyperbolic learning for computer vision, and hyperbolic learning for various domains, ranging from image segmentation and social navigation to hierarchical classification and vision-language alignment.
CVPR: "Hyperbolic Safety-Aware Vision-Language Models" [pdf].
ICLR: "Compositional Entailment Learning for Hyperbolic Vision-Language Models" [pdf] (oral).
ICLR: "Union-over-Intersections: Object Detection beyond Winner-Takes-All" (spotlight) [pdf].
ICLR: "BrainACTIV: Identifying Visuo-Semantic Properties Driving Cortical Selectivity using Diffusion-Based Image Manipulation" [pdf].
Keynote: "Hyperbolic Deep Learning", the BMVA Summer School 2025, Aberdeen.
Keynote: "Hyperbolic Visual Learning", NEGEL Workshop, The Web Conference 2025, Sydney.
IJCV: "SimZSL: Zero-Shot Learning Beyond a Pre-Defined Semantic Embedding Space" [pdf].
ESWC: "Designing Hierarchies for Optimal Hyperbolic Embedding" [pdf].
Talk: "Hyperbolic Deep Learning" Computer Vision by Learning course, Amsterdam.
Talk: "Hyperbolic Deep Learning", TU Delft, Delft.
Workshop: Hyperbolic and Hyperspherical Learning for Computer Vision, ECCV 2024, Milan.
IROS: "Hyp2Nav: Hyperbolic Planning and Curiosity for Crowd Navigation" [pdf].
TMLR: "Hyperbolic Random Forests" [pdf].
TMLR: "Intriguing Properties of Hyperbolic Vision-Language Models" [pdf].
Keynote: "Hyperbolic Deep Learning", OxML Summer School.
Keynote: "Hyperbolic Learning for Vision", Workshop on Human-Centered Vision and Media Technologies, Tokyo.
Keynote: "Hyperbolic Learning for Vision", CV papers challenge workshop, Tokyo.
IJCV: "Hyperbolic Deep Learning in Computer Vision: A Survey" [pdf].
AAAI: "Latent Space Editing in Transformer-Based Flow Matching" [pdf].
Host: prof. Zeynep Akata
Host: prof. Shih-Fu Chang
Promotors: prof. Cees Snoek and prof. Arnold Smeulders
Code and pre-computed prototypes are available [here].
Pre-trained models can be downloaded by following the instructions [here].
Code and pre-computed prototypes are available [here].
The complete Video Water Dataset is available [here] (warning 14GB).