Learning From Unlabeled Videos
CVPR 2021 Workshop
June 20th, 1:50pm (EDT)
News & Updates
June 13, 2021: We have published the workshop schedule below.
Mar 28, 2021: We are extending the paper submission deadline to April 9, 2021.
Mar 4, 2021: Call for Papers has been released! https://groups.google.com/g/ml-news/c/CaEc7WQ78Ew
Jan 11, 2021: Site under construction. Please check soon for more information.
Deep neural networks trained with a large number of labeled images have recently led to breakthroughs in computer vision. However, we have yet to see a similar level of breakthrough in the video domain. Why is this? Should we invest more into supervised learning or do we need a different learning paradigm?
Unlike images, videos contain extra dimensions of information such as motion and sound. Recent approaches leverage such signals to tackle various challenging tasks in an unsupervised/self-supervised setting, e.g., learning to predict certain representations of the future time steps in a video (RGB frame, semantic segmentation map, optical flow, camera motion, and corresponding sound), learning spatio-temporal progression from image sequences, and learning audiovisual correspondences.
This workshop aims to promote comprehensive discussion around this emerging topic. We invite researchers to share their experiences and knowledge in learning from unlabeled videos, and to brainstorm brave new ideas that will potentially generate the next breakthrough in computer vision.
(Eastern Time Zone, UTC−04:00)
2:00-2:30 Invited Speaker 1: Andrew Zisserman. Self-Supervised Video Representation Learning and Beyond. [abstract]
2:30-3:00 Invited Speaker 2: Angjoo Kanazawa. Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image. [abstract]
3:00-3:30 Oral Session 1
3:30 - 4:00 Oral Session 2
4:00 - 4:30 Invited Speaker 3: Bryan Russell. On Being A Couch Potato. [abstract]
4:30 - 5:00 Invited Speaker 4: Shuran Song. In LUV with Interactions.
5:00 - 5:30 Invited Speaker 5: Deepak Pathak. Unifying Perception and Control through Video. [abstract]
5:30 - 6:00 Oral Session 3
6:00 - 6:10 Closing Remarks
University of Oxford
University of Michigan/LG AI Research