Back to ListThe Unreasonable Effectiveness of Large Language-Vision Models for Video Domain Adaptation
Tuesday 24th October 2023 17:00 CET
Professor Elisa Ricci
Video analysis tasks, such as action recognition, have long been investigated in computer vision. Major progress has been made in the last decade with the development of specialized deep architectures, such as 3D CNNs and Video Transformers [trained on large-scale annotated datasets. However, obtaining sufficient labelled training videos for real-world scenarios can be very costly and time consuming.
In order to alleviate the burden of annotating large scale datasets, Video-based Unsupervised Adaptation (VUDA) methods have been introduced.
The VUDA methods are derived from the common idea of transferring knowledge from a labelled source domain to an unlabelled target domain.
In the last few years, the field of computer vision has also witnessed the emergence of a new generation of powerful deep architectures, trained on mammoth internet-scale image-text datasets. These models, commonly known as foundation models or Large Language Vision Models (LLVMs) have achieved outstanding performance, and have become a cornerstone of modern computer vision research. In this talk I will introduce recent works from my research group which leverage LLVMs for addressing the main challenges of VUDA.
Prof. Elisa Ricci (PhD, University of Perugia 2008) is an Associate Professor at Department of Information Engineering and Computer Science (DISI) at the University of Trento and the head of the Deep Visual Learning research group at Fondazione Bruno Kessler. She has published over 160 papers on international venues. Her research interests are mainly in the areas of computer vision, robotic perception and multimedia analysis. At UNITN she is the Coordinator of the Doctoral Program in Information Engineering and Computer Science. She is an Associate Editor of IEEE Trans. on Multimedia, Computer Vision and Image Understanding and Pattern Recognition. She is/was the Program Chair of ECCV 2024, ACM MM 2020 and the Diversity Chair of ACM MM 2022. She is the recipient of the ACM MM 2015 Best Paper award and ICCV 2021 Honorable mention award.
