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Unsupervised Learning of Visual Representations Using Videos
Open accessAbstract
Is strong supervision necessary for learning a good visual representation? Do we really need millions of semantically-labeled images to train a Convolutional Neural Network (CNN)? In this paper, we present a simple yet surprisingly powerful approach for unsupervised learning of CNN. Specifically, we use hundreds of thousands of unlabeled videos from the web to learn visual representations. Our key idea is that visual tracking provides the supervision. That is, two patches connected by a track should have similar visual representation in deep feature space since they probably belong to same object or object part. We design a Siamese-triplet network with a ranking loss function to train this CNN representation. Without using a single image from ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train an ensemble of unsupervised networks that achieves 52% mAP (no bounding box regression). This performance comes tantalizingly close to its ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4%. We also show that our unsupervised network can perform competitively in other tasks such as surface-normal estimation.
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“Our survey found that most deepfake detection methods assume a static game (Mirsky and Lee 2021; Wang and Gupta 2015).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Wang and Gupta 2015
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Metadata
- Authors
- Wang, X., & Gupta, A.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2794-2802
- OpenAlex
- W219040644
- Cited by
- 1,005
- References
- 58
- Citations by year
- 2026: 8 · 2025: 32 · 2024: 44 · 2023: 63 · 2022: 97 · 2021: 146 · 2020: 173 · 2019: 140 · 2018: 121 · 2017: 108 · 2016: 65 · 2015: 8
- FWCI
- 41.29
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1320083)
- Nvidia
- Defense Advanced Research Projects Agency (FA8750-14- 2-0244, FA8750)
- Multidisciplinary University Research Initiative
- Office of Naval Research (N00014-10-10934, MURI N000141010934)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- video-based learning, visual object tracking, ranking loss, object detection, unlabeled video, surface normal estimation, convolutional neural networks
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