Munich AI Lectures

Insights & Ideas
from Experts in the Field

Next Lectures:

Ivan Laptev - From Video Understanding to Embodied Agents

Tuesday, June 25th, 5pm

Arcisstraße 21, 80333 München, Room 0790, https://campus.tum.de/tumonline/pl/ui/$ctx;design=pl;header=max;lang=EN/wbKalender.wbRessource?pResNr=12571, (Eingang Ecke Arcisstr./Theresienstr.)

This event is open to everyone, registration is not required.

Abstract

Computer vision has recently excelled on a wide range of tasks such as image classification, segmentation and captioning. This impressive progress now powers many applications of internet imaging and yet, current methods still fall short in addressing embodied understanding of visual scenes. What will happen if pushing an object over a table border? What precise actions are required to plant a tree? Building systems that can answer such questions from visual inputs will empower future applications of robotics and personal visual assistants while enabling methods to operate in unstructured real-world environments.

Following this motivation, in this talk we will address models and learning methods that derive procedural knowledge from instructional videos. I will then describe our recent work on visual manipulation and will present a new dataset for long-term story-level video understanding.

Bio

Ivan Laptev is a visiting professor at MBZUAI and a senior researcher on leave from Inria Paris. He received a PhD degree in Computer Science from the Royal Institute of Technology in 2004 and a Habilitation degree from École Normale Supérieure in 2013.
 
Ivan’s main research interests include visual recognition of human actions, objects and interactions, and more recently robotics. He has published over 120 technical papers most of which appeared in international journals and major peer-reviewed conferences of the field. He served as an associate editor of IJCV and TPAMI, he served as a program chair for CVPR’18 and ICCV’23, he will serve as a program chair for ACCV’24 and a general chair for ICCV’29. He has co-organized several tutorials, workshops and challenges at major computer vision conferences. He has also co-organized a series of INRIA summer schools on computer vision and machine learning (2010-2013) and Machines Can See summits (2017-2024). He received an ERC Starting Grant in 2012 and was awarded a Helmholtz prize for significant impact on computer vision in 2017.

Alexei A. Efros - We are (still?) not giving data enough credit

Wednesday, July 17th, 6pm

Alfons-Goppel-Straße 11 (Residenz), 80539 München, Plenarsaal of the Bavarian Academy of Sciences and Humanities 

Please register for the event by 27 June 2024 at the latest using the following link: https://ki-agentur.odoo-host.de/event/munich-ai-lecture-alexei-a-efros-21/register

Abstract

Coming soon…

Bio

Alexei (Alyosha) Efros joined UC Berkeley in 2013. Prior to that, he was for a decade on the faculty of Carnegie Mellon University, and has also been affiliated with École Normale Supérieure/INRIA and University of Oxford.

His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available.

Efros received his PhD in 2003 from UC Berkeley. He is a recipient of CVPR Best Paper Award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), SIGGRAPH Significant New Researcher Award (2010), three PAMI Helmholtz Test-of-Time Prizes (1999,2003,2005), the ACM Prize in Computing (2016), Diane McEntyre Award for Excellence in Teaching Computer Science (2019), Jim and Donna Gray Award for Excellence in Undergraduate Teaching of Computer Science (2023), and PAMI Thomas S. Huang Memorial Prize (2023).

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On a monthly basis, we invite top-level AI researchers to give us a glimpse into their work and the future of AI. Join us for our next lecture!

Our lectures consist of a short presentation followed by a Q&A to enable a lively discussion with our speakers. Each lecture lasts about one hour and will be streamed live on our YouTube channel. Recordings will be available  afterwards.