[CFP] CVPR 2021 Workshop on Learning from Unlabeled Videos (LUV)
Yale Song
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Call for papers:
3rd Workshop on Learning from Unlabeled Videos (LUV) @ CVPR 2021
https://sites.google.com/view/luv2021
https://cmt3.research.microsoft.com/LUV2021
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Overview
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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? Do we need even larger labeled video datasets and higher-capacity neural networks? Or do we need a completely different learning paradigm for videos? Will the next breakthrough in computer vision (especially video understanding) still come from supervised learning?
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 audio-visual and text-visual 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.
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Topics
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We invite submissions of 2-4 page extended abstract in topics including (but not limited to):
- Unsupervised and self-supervised learning with unlabeled videos
- Multimodal self-supervision, e.g., sound prediction from video
- Unsupervised visual concept discovery from videos
- Unsupervised visual representation learning
- Causal reasoning from videos
- Learning from noisy web videos
- Learning for actively acquired videos
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Submission Instructions
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All submissions will be handled electronically via the workshop's CMT Website:
https://cmt3.research.microsoft.com/LUV2021
Papers are limited to four pages including references. Please refer to the CVPR style for detailed formatting instructions:
http://cvpr2021.thecvf.com/node/33#submission-guidelines
We accept papers that have been recently published elsewhere or to be presented at CVPR 2021/submitted to ICCV 2021.
Accepted papers will not appear in any proceedings and be considered non-archival. We will ask the authors to publish the paper on the workshop website (arxiv link preferred).
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Important Dates
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(All deadlines are due by 11:59 p.m. Pacific Standard Time on the listed dates)
- Paper submission: Friday, March 26, 2021
- Notification to authors: Friday, April 30, 2021
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Invited Speakers
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- Andrew Zisserman, University of Oxford
- Angjoo Kanazawa, UC Berkeley
- Bryan Russell, Adobe Research
- Deepak Pathak, Carnegie Mellon University
- Shuran Song, Columbia University
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Program Committee
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- AJ Piegiovanni, Google Research
- Allen Jabri, UC Berkeley
- Camille Couprie, Facebook AI Research
- Chen Sun, Brown
- Christoph Feichtenhofer, FAIR
- Jacob Charles Walker, DeepMind
- Ruben Villegas, Adobe Research
- Ruohan Gao, UT Austin
- Tushar Nagarajan, UT Austin
- Yannis Kalantidis, NAVER Labs Europe
- Yunseok Jang, University of Michigan
(More to be added)
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Organizers
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- Yale Song, Microsoft Research
- Mia Chiquier, Columbia University
- Sachit Menon, Columbia University
- Carl Vondrick, Columbia University
- Anelia Angelova, Google Research
- Honglak Lee, University of Michigan / LG AI Research
- Kristen Grauman, UT Austin / Facebook AI Research