Pia Bideau

Postdoctoral Researcher

mail: p.bideau@tu-berlin.de

Pia Bideau is a postdoctoral researcher at TU Berlin and part of the Cluster Science of Intelligence as of January 2020. Her research aims 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.

At SCIoI, Pia is working on Project 08, Project 29, Project 33, and Project 35.


Website: https://people.cs.umass.edu/~pbideau/


SCIoI Publications:

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
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
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