About the speaker: Prof. Dr. Laura Leal-Taixé is a Senior Research Manager at NVIDIA and also an Adjunct Professor at the Technical University of Munich (TUM), leading the Dynamic Vision and Learning group. From 2018 until 2022, she was a tenure-track professor at TUM. Before that, she spent two years as a postdoctoral researcher at ETH Zurich, Switzerland, and a year as a senior postdoctoral researcher in the Computer Vision Group at the Technical University in Munich. She obtained her PhD from the Leibniz University of Hannover in Germany, spending a year as a visiting scholar at the University of Michigan, Ann Arbor, USA. She pursued B.Sc. and M.Sc. in Telecommunications Engineering at the Technical University of Catalonia (UPC) in her native city of Barcelona. She is a recipient of the Sofja Kovalevskaja Award of 1.65 million euros in 2017, the Google Faculty Award in 2021, and the ERC Starting Grant in 2021. Her research interests lie in video understanding and visual localization.

About the keynote: The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatiotemporal trajectories. This problem has been traditionally addressed with the tracking-by-detection paradigm, but recent research has focused on more recent end-to-end learning paradigms such as tracking-by-regression or tracking-by-attention. In this talk I will discuss all the paradigms shifts and show how the community evolved towards end-to-end learning for MOT, with its advantages and disadvantages.