People

Pia Bideau

External Collaborator

Computer Vision
Robotics

TU Berlin

   

Photo: SCIoI

← People Overview

Pia Bideau

Pia Bideau

Photo: SCIoI

Pia Bideau is a former SCIoI PostDoc and currently works with SCIoI as an external collaborator. Pia Bideau worked as a postdoctoral researcher at TU Berlin and part of the Cluster Science of Intelligence. Her research aimed to address the topic of how one can teach a computer to see and understand the world as we humans do, the strengths and weaknesses of a computer vision system compared to a human vision system, and how the two systems can learn from each other. We move, we discover new interesting stuff that raises our curiosity if a perceived situation doesn’t match certain expectations, and we learn. Pia’s research focuses on motion – our motion as well as our motion perception. Motion is a key ability that we as living beings have to explore our environment. Our motion for example helps us to perceive depth, and the motion of objects helps us to recognize these objects even if those are unknown to us. Motion in the visual world  helps us understanding an unstructured environment we are living in. Before she joined the Cluster of Intelligence, Pia received her PhD from the University of Massachusetts, Amherst (USA) working with Prof. Erik Learned-Miller and worked together with Cordelia Schmid and Karteek Alahari as part of an internship at Inria in Grenoble (France). To learn more about Pia, please visit her hompage. Website https//people.cs.umass.edu/~pbideau/

Projects

Pia Bideau is member of:

• Project S2: Support Synthetic Research
• Project 33: Shepherding behaviour in predator-prey interactions
• Project 35: Differentiable interconnected recursive estimation as a principle of intelligence
Cai, N., & Bideau, P. (2025). Local Event Alignment for Monocular Distance estimation. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025.
Pacher, K., Krause, J., Bartashevich, P., Romanczuk, P., Bideau, P., Pham, D., Burns, A., Deffner, D., Dhellemmes, F., Binder, B., Boswell, K., Galván-Magaña, F., Domenici, P., & Hansen, M. (2024). Evidence for a by-product mutualism in a group hunter depends on prey movement state. Functional Ecology. https://doi.org/10.1111/1365-2435.14638
Pham, D., Hansen, M., Dhellemmes, F., Krause, J., & Bideau, P. (2024). Watching Swarm Dynamics from Above: A Framework for Advances Object Tracking in Drone Videos. IEEE Computer Vision and Pattern Recognition Conference Workshops (CVPRW) 2024.
Maier, M., Leonhardt, A., Blume, F., Bideau, P., Hellwich, O., & Rahman, R. A. (2024). Brain dynamics of mental state attribution during perception of social robot faces. OSF. https://doi.org/10.31219/osf.io/2rxy9
Halawa, M., Blume, F., Bideau, P., Maier, M., Abdel Rahman, R., & Hellwich, O. (2024). Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition. IEEE Computer Vision and Pattern Recognition Conference Workshops (CVPRW) 2024. https://doi.org/10.48550/arXiv.2404.10904
Blume, F., Qu, R., Bideau, P., Maier, M., Abdel Rahman, R., & Hellwich, O. (2024). How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal Context by Modeling Mental Representations. GCPR 2024.
Maier, M., Blume, F., Bideau, P., Hellwich, O., & Abdel Rahman, R. (2022). Knowledge-Augmented Face Perception: Prospects for the Bayesian Brain-Framework to Align AI and Human Vision. Consciousness and Cognition, 101. https://doi.org/10.1016/j.concog.2022.103301
Halawa, M., Hellwich, O., & Bideau, P. (2022). Action based Contrastive Learning for Trajectory Prediction. European Conference on Computer Vision (ECCV), 143–159. https://doi.org/10.1007/978-3-031-19842-7_9
Roth, N., Bideau, P., Hellwich, O., Rolfs, M., & Obermayer, K. (2021). Modeling the influence of objects on saccadic decisions in dynamic real-world scenes. PERCEPTION / 43rd European Conference on Visual Perception (ECVP) 2021. https://journals.sagepub.com/doi/full/10.1177/03010066211059887
Roth, N., Bideau, P., Hellwich, O., Rolfs, M., & Obermayer, K. (2021). A modular framework for object-based saccadic decisions in dynamic scenes. CVPR EPIC Workshop / ArXiv:2106.06073. https://doi.org/10.48550/arXiv.2106.06073
Gu, C., Learned-Miller, E., Gallego, G., Sheldon, D., & Bideau, P. (2021). The Spatio-Temporal Poisson Point process: A simple Model for the Alignment of Event Camera Data. International Conference on Computer Vision (ICCV), 13495–13504. https://doi.org/10.1109/ICCV48922.2021.01324

X-Student Research Group Grants (BUA, winter 2023/2024)

X-Student Research Group Grants (BUA, summer 2023)

Research Fellow Chair MIAI (Grenoble Alpes, France)

Research

An overview of our scientific work

See our Research Projects