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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
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
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- lecun, 1990, Handwritten digit recognition with a backpropagation network, NIPS
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Items 801–900 of 1,002
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Koohzadi, M., & Charkari, N. M. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10248-1 · Cited by 2
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Li, Z., Cao, L., Wang, H., & Xu, L. (2022) · Mathematics · doi:10.3390/math10122002 · Cited by 2
CEDR: Contrastive Embedding Distribution Refinement for 3D point cloud representation
Yang, F., Cao, Y., Xue, Q., Jin, S., Li, X., & Zhang, W. (2024) · Signal Processing Image Communication · doi:10.1016/j.image.2024.117129 · Cited by 2
Learning to mask and permute visual tokens for Vision Transformer pre-training
Baraldi, L., Amoroso, R., Cornia, M., Baraldi, L., Pilzer, A., & Cucchiara, R. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104294 · Cited by 4
MHNet: A Masked Hybrid Network for Robust Water Body Segmentation From Aerial Images
Wang, S., Wei, B., Shi, B., Wang, N., Zhang, Y., & Zhu, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3580479 · Cited by 3
Unsupervised Feature Learning in Computer Vision
Goroshin, R. (2015) · Cited by 2
Part-Based Data Association for Visual Tracking
Jiang, Z., Huynh, D. Q., Zhang, J., & Wu, Q. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227474 · Cited by 2
Object Detection Based on CNNs: Current and Future Directions
Chen, L., Sadka, A. H., Dong, J., & Zhou, H. (2018) · Springer eBooks · doi:10.1007/978-3-319-89914-5_2 · Cited by 3
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Leordeanu, M. (2020) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-42128-1_7 · Cited by 2
Yang, Y. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00174 · Cited by 2
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Lin, F., Xu, H., Li, H., Xiong, H., & Qi, G.-J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17044 · Cited by 2
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Das, R. K. K., Kumari, K., Manjhi, P. K., & Thepade, S. D. (2019) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon46936.2019.9105759 · Cited by 2
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Jiao, J., Bao, L., Wei, Y., He, S., Shi, H., Lau, R. W. H., & Huang, T. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13623 · Cited by 2
Jia, X., Hong, H., & DiCarlo, J. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.13.900837 · Cited by 2
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Jeong, Y., Kim, Y., & Song, H. O. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01164 · Cited by 1
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Chen, Q., & Yang, R. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00168 · Cited by 1
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Geng, S., Zhao, S., & Hu, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02790-9 · Cited by 1
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Betti, A., Boccignone, G., Faggi, L., Gori, M., & Melacci, S. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.768516 · Cited by 1
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Yuan, M., Bi, X., Huang, X., Zhang, W., Hu, L., Yuan, G., Zhao, X., & Sun, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30637-2_16 · Cited by 1
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Chen, G., Liu, X., Xu, X., Cheung, Y., & Li, T. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611938 · Cited by 1
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Zhang, L. (2020) · Journal of International Crisis and Risk Communication Research · Cited by 1
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Xu, Y., Wu, Y., Zuraimi, N. S. B., Nobuhara, S., & Nishino, K. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01719-0 · Cited by 2
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Karaman, K., Akkaya, İ. B., Solmaz, B., & Alatan, A. A. (2020) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu49456.2020.9302060 · Cited by 1
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Yang, F. (2020) · Journal of Bioresource Management · Cited by 1
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Mendonça, M. O. K., Ferreira, J. O., & Diniz, P. S. R. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616350 · Cited by 1
Zach, J., Busse, C., Funk, S., Möllmann, C., Renner, C., & Tiedemann, T. (2021) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans44145.2021.9705867 · Cited by 1
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Li, Q., & Qiu, G. (2022) · Remote Sensing · doi:10.3390/rs14143317 · Cited by 2
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Lee, J., Lee, J., Lee, S., & Yoon, S. (2018) · arXiv (Cornell University) · Cited by 2
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Caron, M. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
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Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.07017 · Cited by 1
Imbert, J. (2019) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
Lin, F., Xu, H., Li, H., Xiong, H., & Qi, G.-J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07004 · Cited by 1
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Planamente, M., Bottino, A., & Barbara Caputo, B. (2020) · arXiv (Cornell University) · Cited by 1
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Bansal, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13919 · Cited by 1
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Zhu, J., Li, Y., & Zhou, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.07477 · Cited by 1
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Huang, J. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/164573 · Cited by 1
Forest, F. (2021) · theses.fr (ABES) · doi:10.70675/5187c565z8bbaz452bzaa6ez7a367a0b16f0 · Cited by 1
Learning visual features under motion invariance
Betti, A., Gori, M., & Melacci, S. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.03.013 · Cited by 8
Autoconvolution for Unsupervised Feature Learning.Preprint
Knyazev, B. A., Barth, E., & Martinetz, T. (2016) · arXiv (Cornell University)
Unsupervised learning of image motion by recomposing sequences.
Jaegle, A., Phillips, S., Ippolito, D., & Daniilidis, K. (2016)
Combining sparse appearance and Bayesian inference models for object tracking
Jiang, Z., Xu, B., & Gong, S. (2016) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais.2016.7822439
Machine learning solutions to visual recognition problems
Verbeek, J. J. (2016) · HAL
Discriminate-and-Rectify Encoders: Learning from Image Transformation SetsPreprint
Tacchetti, A., Voinea, S., & Evangelopoulos, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.04775 · Cited by 1
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Liu, S., & Lu, H. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019445
PBG-Net: Object detection with a multi-feature and iterative CNN model
Lou, Y., Fu, G., Jiang, Z., Men, A., & Zhou, Y. F. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026284
Pedestrian detection with dynamic iterative bootstrapping
Pei, C., Hao, L., & Zhu, Y. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297070
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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