no code implementations • ICCV 2021 • Kirill Gavrilyuk, Mihir Jain, Ilia Karmanov, Cees G. M. Snoek
With the motion model we generate pseudo-labels for a large unlabeled video collection, which enables us to transfer knowledge by learning to predict these pseudo-labels with an appearance model.
no code implementations • CVPR 2020 • Kirill Gavrilyuk, Ryan Sanford, Mehrsan Javan, Cees G. M. Snoek
This paper strives to recognize individual actions and group activities from videos.
no code implementations • CVPR 2020 • Tom F. H. Runia, Kirill Gavrilyuk, Cees G. M. Snoek, Arnold W. M. Smeulders
For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior.
no code implementations • 17 Oct 2019 • Tom F. H. Runia, Kirill Gavrilyuk, Cees G. M. Snoek, Arnold W. M. Smeulders
Nevertheless, inferring specifics from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces.
1 code implementation • CVPR 2018 • Kirill Gavrilyuk, Amir Ghodrati, Zhenyang Li, Cees G. M. Snoek
This paper strives for pixel-level segmentation of actors and their actions in video content.
Ranked #13 on Referring Expression Segmentation on J-HMDB