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Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance.
S. Pal, A. Datta, and D. D. Majumder, “Computer recognition of vowel sounds using a self-supervised learning algorithm,” Journal of the Anatomical Society of India
1978
Earlier work this paper cites.
A. Ghosh, N. R. Pal, and S. K. Pal, “Self-organization for object extraction using a multilayer neural network and fuzziness mearsures,” IEEE Transactions on Fuzzy Systems
1993
Earlier work this paper cites.
V. R. de Sa, “Learning classification with unlabeled data,” in Neural Inf. Process. Syst
1994
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science
2006
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in IEEE Conf. Comput. Vis. Pattern Recognit
2006
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Int. Conf. Mach. Learn
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE Conf. Comput. Vis. Pattern Recognit
2009
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in Int. Conf. Artif. Intell. Statist
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” Int. J. Comput. Vis
2010
Earlier work this paper cites.
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “Hmdb: a large video database for human motion recognition,” in IEEE Int. Conf. Comput. Vis
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Neural Inf. Process. Syst
2013
Earlier work this paper cites.
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with convolutional neural networks,” in Neural Inf. Process. Syst
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Neural Inf. Process. Syst
2014
Earlier work this paper cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in IEEE Int. Conf. Comput. Vis
2015
Earlier work this paper cites.
P. Agrawal, J. Carreira, and J. Malik, “Learning to see by moving,” in IEEE Int. Conf. Comput. Vis
2015
Earlier work this paper cites.
A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with exemplar convolutional neural networks,” IEEE Trans. Pattern Anal. Mach. Intell
2015
Earlier work this paper cites.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in IEEE Int. Conf. Comput. Vis
2015
Earlier work this paper cites.
J. Walker, A. Gupta, and M. Hebert, “Dense optical flow prediction from a static image,” in IEEE Int. Conf. Comput. Vis
2015
Earlier work this paper cites.
D. Jayaraman and K. Grauman, “Learning image representations tied to ego-motion,” in IEEE Int. Conf. Comput. Vis
2015
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, “Microsoft coco: Common objects in context,” 2015
2015
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and learn: unsupervised learning using temporal order verification,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in IEEE Int. Conf. Robot. Autom
2016
Earlier work this paper cites.
Y. Li, M. Paluri, J. M. Rehg, and P. Dollár, “Unsupervised learning of edges,” in IEEE Conf. Comput. Vis. Pattern Recognit
2016
Earlier work this paper cites.
D. Li, W.-C. Hung, J.-B. Huang, S. Wang, N. Ahuja, and M.-H. Yang, “Unsupervised visual representation learning by graph-based consistent constraints,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
G. Larsson, M. Maire, and G. Shakhnarovich, “Learning representations for automatic colorization,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in IEEE Conf. Comput. Vis. Pattern Recognit
2016
Earlier work this paper cites.
J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in Int. Conf. Mach. Learn
2016
Earlier work this paper cites.
J. Yang, D. Parikh, and D. Batra, “Joint unsupervised learning of deep representations and image clusters,” in IEEE Conf. Comput. Vis. Pattern Recognit
2016
Earlier work this paper cites.
A. Sharma, O. Grau, and M. Fritz, “Vconv-dae: Deep volumetric shape learning without object labels,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta, “Learning a predictable and generative vector representation for objects,” in Eur. Conf. Comput. Vis
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Larsson, M. Maire, and G. Shakhnarovich, “Colorization as a proxy task for visual understanding,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
M. Noroozi, H. Pirsiavash, and P. Favaro, “Representation learning by learning to count,” in IEEE Int. Conf. Comput. Vis
2017
Earlier work this paper cites.
P. Bojanowski and A. Joulin, “Unsupervised learning by predicting noise,” in Int. Conf. Mach. Learn
2017
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Split-brain autoencoders: Unsupervised learning by cross-channel prediction,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
X. Wang, K. He, and A. Gupta, “Transitive invariance for self-supervised visual representation learning,” in IEEE Int. Conf. Comput. Vis
2017
Earlier work this paper cites.
L. Gomez, Y. Patel, M. Rusiñol, D. Karatzas, and C. Jawahar, “Self-supervised learning of visual features through embedding images into text topic spaces,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
K. Gong, X. Liang, D. Zhang, X. Shen, and L. Lin, “Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan, “Learning features by watching objects move,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
I. Croitoru, S.-V. Bogolin, and M. Leordeanu, “Unsupervised learning from video to detect foreground objects in single images,” in IEEE Int. Conf. Comput. Vis
2017
Earlier work this paper cites.
B. Fernando, H. Bilen, E. Gavves, and S. Gould, “Self-supervised video representation learning with odd-one-out networks,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
H.-Y. Lee, J.-B. Huang, M. Singh, and M.-H. Yang, “Unsupervised representation learning by sorting sequences,” in IEEE Int. Conf. Comput. Vis
2017
Earlier work this paper cites.
R. Arandjelovic and A. Zisserman, “Look, listen and learn,” in IEEE Int. Conf. Comput. Vis
2017
Earlier work this paper cites.
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba, “Network dissection: Quantifying interpretability of deep visual representations,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, “Scene parsing through ade20k dataset,” in IEEE Conf. Comput. Vis. Pattern Recognit
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Goyal, S. Ebrahimi Kahou, V. Michalski, J. Materzynska, S. Westphal, H. Kim, V. Haenel, I. Fruend, P. Yianilos, M. Mueller-Freitag, et al
2017
Earlier work this paper cites.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” in Int. Conf. Learn. Represent
2018
Earlier work this paper cites.
D. Wei, J. J. Lim, A. Zisserman, and W. T. Freeman, “Learning and using the arrow of time,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Nathan Mundhenk, D. Ho, and B. Y. Chen, “Improvements to context based self-supervised learning,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
R. Arandjelovic and A. Zisserman, “Objects that sound,” in Eur. Conf. Comput. Vis
2018
Earlier work this paper cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Eur. Conf. Comput. Vis
2018
Earlier work this paper cites.
P. Krähenbühl, “Free supervision from video games,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
M. Gadelha, R. Wang, and S. Maji, “Multiresolution tree networks for 3d point cloud processing,” in Eur. Conf. Comput. Vis
2018
Earlier work this paper cites.
Z. Ren and Y. Jae Lee, “Cross-domain self-supervised multi-task feature learning using synthetic imagery,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
M. Noroozi, A. Vinjimoor, P. Favaro, and H. Pirsiavash, “Boosting self-supervised learning via knowledge transfer,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
U. Buchler, B. Brattoli, and B. Ommer, “Improving spatiotemporal self-supervision by deep reinforcement learning,” in Eur. Conf. Comput. Vis
2018
Earlier work this paper cites.
X. Liang, K. Gong, X. Shen, and L. Lin, “Look into person: Joint body parsing & pose estimation network and a new benchmark,” IEEE Trans. Pattern Anal. Mach. Intell
2018
Earlier work this paper cites.
Z. Yin and J. Shi, “Geonet: Unsupervised learning of dense depth, optical flow and camera pose,” in IEEE Conf. Comput. Vis. Pattern Recognit
2018
Earlier work this paper cites.
B. Korbar, D. Tran, and L. Torresani, “Cooperative learning of audio and video models from self-supervised synchronization,” in Neural Inf. Process. Syst
2018
Earlier work this paper cites.
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine, “Time-contrastive networks: Self-supervised learning from video,” in IEEE Int. Conf. Robot. Autom
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
C. Gu, C. Sun, D. A. Ross, C. Vondrick, C. Pantofaru, Y. Li, S. Vijayanarasimhan, G. Toderici, S. Ricco, R. Sukthankar, et al
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
P. Goyal, D. Mahajan, A. Gupta, and I. Misra, “Scaling and benchmarking self-supervised visual representation learning,” in IEEE Int. Conf. Comput. Vis
2019
Earlier work this paper cites.
U. Ahsan, R. Madhok, and I. Essa, “Video jigsaw: Unsupervised learning of spatiotemporal context for video action recognition,” in Proc. Winter Conf. Appl. Comput. Vis
2019
Earlier work this paper cites.
X. Zhan, X. Pan, Z. Liu, D. Lin, and C. C. Loy, “Self-supervised learning via conditional motion propagation,” in IEEE Conf. Comput. Vis. Pattern Recognit
2019
Earlier work this paper cites.
K. Wang, L. Lin, C. Jiang, C. Qian, and P. Wei, “3d human pose machines with self-supervised learning,” IEEE Trans. Pattern Anal. Mach. Intell
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
N. Saunshi, O. Plevrakis, S. Arora, M. Khodak, and H. Khandeparkar, “A theoretical analysis of contrastive unsupervised representation learning,” in Int. Conf. Mach. Learn
2019
Earlier work this paper cites.
A. Kolesnikov, X. Zhai, and L. Beyer, “Revisiting self-supervised visual representation learning,” in IEEE Conf. Comput. Vis. Pattern Recognit
2019
Earlier work this paper cites.
T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby, “Self-supervised gans via auxiliary rotation loss,” in IEEE Conf. Comput. Vis. Pattern Recognit
2019
Earlier work this paper cites.
X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, “S4l: Self-supervised semi-supervised learning,” in IEEE Int. Conf. Comput. Vis
2019
Earlier work this paper cites.
D. Hendrycks, M. Mazeika, S. Kadavath, and D. Song, “Using self-supervised learning can improve model robustness and uncertainty,” in Neural Inf. Process. Syst
2019
Earlier work this paper cites.
L. Zhang and Z. Zhu, “Unsupervised feature learning for point cloud understanding by contrasting and clustering using graph convolutional neural networks,” in International Conference on 3D Vision
2019
Earlier work this paper cites.
Y. Zhao, T. Birdal, H. Deng, and F. Tombari, “3d point capsule networks,” in IEEE Conf. Comput. Vis. Pattern Recognit
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord, “Boosting few-shot visual learning with self-supervision,” in IEEE Int. Conf. Comput. Vis
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. Han, W. Xie, and A. Zisserman, “Video representation learning by dense predictive coding,” in ICCV Workshops
2019
Earlier work this paper cites.
D. Xu, J. Xiao, Z. Zhao, J. Shao, D. Xie, and Y. Zhuang, “Self-supervised spatiotemporal learning via video clip order prediction,” in IEEE Conf. Comput. Vis. Pattern Recognit
2019
Earlier work this paper cites.
A. Diba, V. Sharma, L. V. Gool, and R. Stiefelhagen, “Dynamonet: Dynamic action and motion network,” in IEEE Int. Conf. Comput. Vis
2019
Earlier work this paper cites.
C. Sun, A. Myers, C. Vondrick, K. Murphy, and C. Schmid, “Videobert: A joint model for video and language representation learning,” in IEEE Int. Conf. Comput. Vis
2019
Earlier work this paper cites.
X. Wang, A. Jabri, and A. A. Efros, “Learning correspondence from the cycle-consistency of time,” in IEEE Conf. Comput. Vis. Pattern Recognit
2019
Earlier work this paper cites.
X. Li, S. Liu, S. De Mello, X. Wang, J. Kautz, and M.-H. Yang, “Joint-task self-supervised learning for temporal correspondence,” in Neural Inf. Process. Syst
2019
Cited alongside, same era.
N. Pappas and J. Henderson, “Gile: A generalized input-label embedding for text classification,” Transactions of the Association for Computational Linguistics
2019
Cited alongside, same era.
B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, and A. Torralba, “Semantic understanding of scenes through the ade20k dataset,” Int. J. Comput. Vis
2019
Cited alongside, same era.
X. Zeng, Y. Pan, M. Wang, J. Zhang, and Y. Liu, “Realistic face reenactment via self-supervised disentangling of identity and pose,” in AAAI Conf.Artif. Intell
2020
Cited alongside, same era.
A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic, and A. Zisserman, “End-to-end learning of visual representations from uncurated instructional videos,” in IEEE Conf. Comput. Vis. Pattern Recognit
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
R. Zhu, B. Zhao, J. Liu, Z. Sun, and C. W. Chen, “Improving contrastive learning by visualizing feature transformation,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
M. Yang, Y. Li, Z. Huang, Z. Liu, P. Hu, and X. Peng, “Partially view-aligned representation learning with noise-robust contrastive loss,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
A. Islam, C.-F. Chen, R. Panda, L. Karlinsky, R. Radke, and R. Feris, “A broad study on the transferability of visual representations with contrastive learning,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
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alphaXiv is searching for related work…
2020
Cited alongside, same era.
Y. M. Asano, C. Rupprecht, and A. Vedaldi, “A critical analysis of self-supervision, or what we can learn from a single image,” in Int. Conf. Learn. Represent
2020
Cited alongside, same era.
B. Zoph, G. Ghiasi, T.-Y. Lin, Y. Cui, H. Liu, E. D. Cubuk, and Q. Le, “Rethinking pre-training and self-training,” in Neural Inf. Process. Syst
2020
Cited alongside, same era.
A. E. Orhan, V. V. Gupta, and B. M. Lake, “Self-supervised learning through the eyes of a child,” in Neural Inf. Process. Syst
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A survey on contrastive self-supervised learning,” Technologies
2020
Cited alongside, same era.
I. Misra and L. v. d. Maaten, “Self-supervised learning of pretext-invariant representations,” in IEEE Conf. Comput. Vis. Pattern Recognit
2020
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in IEEE Conf. Comput. Vis. Pattern Recognit
2020
Cited alongside, same era.
2021
Later among the works it cites.
J. Zhang, X. Xu, F. Shen, Y. Yao, J. Shao, and X. Zhu, “Video representation learning with graph contrastive augmentation,” in ACM Int. Conf. Multimedia
2021
Later among the works it cites.
Q. Hu, X. Wang, W. Hu, and G.-J. Qi, “Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in Int. Conf. Mach. Learn
2021
Later among the works it cites.
2021
Later among the works it cites.
H. Chen, Y. Wang, B. Lagadec, A. Dantcheva, and F. Bremond, “Joint generative and contrastive learning for unsupervised person re-identification,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
J. J. Sun, A. Kennedy, E. Zhan, D. J. Anderson, Y. Yue, and P. Perona, “Task programming: Learning data efficient behavior representations,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
C. Li, T. Tang, G. Wang, J. Peng, B. Wang, X. Liang, and X. Chang, “Bossnas: Exploring hybrid cnn-transformers with block-wisely self-supervised neural architecture search,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
L. Fan, S. Liu, P.-Y. Chen, G. Zhang, and C. Gan, “When does contrastive learning preserve adversarial robustness from pretraining to finetuning?,” in Neural Inf. Process. Syst
2021
Later among the works it cites.
Y. Lin, X. Guo, and Y. Lu, “Self-supervised video representation learning with meta-contrastive network,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
Y. An, H. Xue, X. Zhao, and L. Zhang, “Conditional self-supervised learning for few-shot classification,” in Int. Joint Conf. Artif. Intell
2021
Later among the works it cites.
Z. Chen, X. Ye, L. Du, W. Yang, L. Huang, X. Tan, Z. Shi, F. Shen, and E. Ding, “Aggnet for self-supervised monocular depth estimation: Go an aggressive step furthe,” in ACM Int. Conf. Multimedia
2021
Later among the works it cites.
H. Chen, B. Lagadec, and F. Bremond, “Ice: Inter-instance contrastive encoding for unsupervised person re-identification,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
T. Isobe, D. Li, L. Tian, W. Chen, Y. Shan, and S. Wang, “Towards discriminative representation learning for unsupervised person re-identification,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
T. Huang, S. Li, X. Jia, H. Lu, and J. Liu, “Neighbor2neighbor: Self-supervised denoising from single noisy images,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
C. Yang, Z. Wu, B. Zhou, and S. Lin, “Instance localization for self-supervised detection pretraining,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
L. Huang, Y. Liu, B. Wang, P. Pan, Y. Xu, and R. Jin, “Self-supervised video representation learning by context and motion decoupling,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
K. Hu, J. Shao, Y. Liu, B. Raj, M. Savvides, and Z. Shen, “Contrast and order representations for video self-supervised learning,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
2021
Later among the works it cites.
H.-Y. Zhou, C. Lu, S. Yang, X. Han, and Y. Yu, “Preservational learning improves self-supervised medical image models by reconstructing diverse contexts,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
O. Manas, A. Lacoste, X. Giró-i Nieto, D. Vazquez, and P. Rodriguez, “Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,” in IEEE Int. Conf. Comput. Vis
2021
Later among the works it cites.
J. Wang, Y. Gao, K. Li, J. Hu, X. Jiang, X. Guo, R. Ji, and X. Sun, “Enhancing unsupervised video representation learning by decoupling the scene and the motion,” in Proceedings of the AAAI Conference on Artificial Intelligence
2021
Later among the works it cites.
J. Knights, B. Harwood, D. Ward, A. Vanderkop, O. Mackenzie-Ross, and P. Moghadam, “Temporally coherent embeddings for self-supervised video representation learning,” in 2020 25th International Conference on Pattern Recognition (ICPR)
2021
Later among the works it cites.
A. Recasens, P. Luc, J.-B. Alayrac, L. Wang, F. Strub, C. Tallec, M. Malinowski, V. Pătrăucean, F. Altché, M. Valko, et al
2021
Later among the works it cites.
C. Feichtenhofer, H. Fan, B. Xiong, R. Girshick, and K. He, “A large-scale study on unsupervised spatiotemporal representation learning,” in Proceedings of the IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
R. Qian, T. Meng, B. Gong, M.-H. Yang, H. Wang, S. Belongie, and Y. Cui, “Spatiotemporal contrastive video representation learning,” in Proceedings of the IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka, and S. Sra, “Can contrastive learning avoid shortcut solutions?,” in Neural Inf. Process. Syst
2021
Later among the works it cites.
T. Chen, C. Luo, and L. Li, “Intriguing properties of contrastive losses,” in Neural Inf. Process. Syst
2021
Later among the works it cites.
Y. Tian, X. Chen, and S. Ganguli, “Understanding self-supervised learning dynamics without contrastive pairs,” in Int. Conf. Mach. Learn
2021
Later among the works it cites.
X. Wang, R. Zhang, C. Shen, T. Kong, and L. Li, “Dense contrastive learning for self-supervised visual pre-training,” in IEEE Conf. Comput. Vis. Pattern Recognit
2021
Later among the works it cites.
D. Chicco, “Siamese neural networks: An overview,” Artificial neural networks
2021
Later among the works it cites.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE T. Knowl. Data Eng
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and P. Yu, “Graph self-supervised learning: A survey,” IEEE T. Knowl. Data Eng
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A review on generative adversarial networks: Algorithms, theory, and applications,” IEEE T. Knowl. Data Eng
2022
Later among the works it cites.
A. Bardes, J. Ponce, and Y. LeCun, “Vicreg: Variance-invariance-covariance regularization for self-supervised learning,” in Int. Conf. Learn. Represent
2022
Later among the works it cites.
X. Wang and G.-J. Qi, “Contrastive learning with stronger augmentations,” IEEE Trans. Pattern Anal. Mach. Intell
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in IEEE Conf. Comput. Vis. Pattern Recognit
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Wang, X. Shen, S. X. Hu, Y. Yuan, J. L. Crowley, and D. Vaufreydaz, “Self-supervised transformers for unsupervised object discovery using normalized cut,” in IEEE Conf. Comput. Vis. Pattern Recognit
2022
Later among the works it cites.
Y. Xu, Q. Zhang, J. Zhang, and D. Tao, “Regioncl: exploring contrastive region pairs for self-supervised representation learning,” in Eur. Conf. Comput. Vis
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Jing, P. Vincent, Y. LeCun, and Y. Tian, “Understanding dimensional collapse in contrastive self-supervised learning,” in Int. Conf. Learn. Represent
2022
Later among the works it cites.
J. Zhou, C. Wei, H. Wang, W. Shen, C. Xie, A. Yuille, and T. Kong, “ibot: Image bert pre-training with online tokenizer,” in Int. Conf. Learn. Represent
2022
Later among the works it cites.
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” in Int. Conf. Learn. Represent
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Xie, Z. Zhang, Y. Cao, Y. Lin, J. Bao, Z. Yao, Q. Dai, and H. Hu, “Simmim: A simple framework for masked image modeling,” in IEEE Conf. Comput. Vis. Pattern Recognit
2022
Later among the works it cites.
C. Wei, H. Fan, S. Xie, C.-Y. Wu, A. Yuille, and C. Feichtenhofer, “Masked feature prediction for self-supervised visual pre-training,” in IEEE Conf. Comput. Vis. Pattern Recognit
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Chen, Y. Liu, D. Jiang, X. Zhang, W. Dai, H. Xiong, and Q. Tian, “Sdae: Self-distillated masked autoencoder,” in Eur. Conf. Comput. Vis
2022
Later among the works it cites.
Q. Zhou, C. Yu, H. Luo, Z. Wang, and H. Li, “Mimco: Masked image modeling pre-training with contrastive teacher,” in ACM Int. Conf. Multimedia
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Liang, S. Zhao, B. Yu, J. Zhang, and F. He, “Meshmae: Masked autoencoders for 3d mesh data analysis,” in Eur. Conf. Comput. Vis
2022
Later among the works it cites.
Y. Pang, W. Wang, F. E. Tay, W. Liu, Y. Tian, and L. Yuan, “Masked autoencoders for point cloud self-supervised learning,” in Eur. Conf. Comput. Vis
2022
Later among the works it cites.
R. Wang, D. Chen, Z. Wu, Y. Chen, X. Dai, M. Liu, Y.-G. Jiang, L. Zhou, and L. Yuan, “Bevt: Bert pretraining of video transformers,” in Proceedings of the IEEE Conf. Comput. Vis. Pattern Recognit
2022
Later among the works it cites.
Z. Tong, Y. Song, J. Wang, and L. Wang, “Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training,” Neural Inf. Process. Syst
2022
Later among the works it cites.
Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong, et al
2022
Later among the works it cites.
Y. Li, H. Mao, R. Girshick, and K. He, “Exploring plain vision transformer backbones for object detection,” in Eur. Conf. Comput. Vis
2022
Later among the works it cites.
Y. Xu, J. Zhang, Q. Zhang, and D. Tao, “Vitpose: Simple vision transformer baselines for human pose estimation,” in Neural Inf. Process. Syst
2022
Later among the works it cites.
L. Wang, F. Liang, Y. Li, H. Zhang, W. Ouyang, and J. Shao, “Repre: Improving self-supervised vision transformer with reconstructive pre-training,” Jan. 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
X. He, Y. Pan, M. Tang, Y. Lv, and Y. Peng, “Learn from unlabeled videos for near-duplicate video retrieval,” in International Conference on Research on Development in Information Retrieval
2022
Later among the works it cites.
D. Wang, Q. Zhang, Y. Xu, J. Zhang, B. Du, D. Tao, and L. Zhang, “Advancing plain vision transformer toward remote sensing foundation model,” IEEE Trans. Geoscience and Remote Sensing
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Tao, H. Wang, X. Zhu, J. Dong, S. Song, G. Huang, and J. Dai, “Exploring the equivalence of siamese self-supervised learning via a unified gradient framework,” in Proceedings of the IEEE Conf. Comput. Vis. Pattern Recognit
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
R. Girdhar, A. El-Nouby, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, “Omnimae: Single model masked pretraining on images and videos,” in Proceedings of the IEEE Conf. Comput. Vis. Pattern Recognit
2023
Closest in time.
A. Gupta, J. Wu, J. Deng, and L. Fei-Fei, “Siamese masked autoencoders,” in Neural Inf. Process. Syst
2023
Closest in time.
Z. Liu, J. Gui, and H. Luo, “Good helper is around you: Attention-driven masked image modeling,” in AAAI Conf.Artif. Intell
2023
Closest in time.
2023
Closest in time.
Z. Xie, Z. Zhang, Y. Cao, Y. Lin, Y. Wei, Q. Dai, and H. Hu, “On data scaling in masked image modeling,” in IEEE Conf. Comput. Vis. Pattern Recognit
2023
Closest in time.
2023
Closest in time.
X. Kong and X. Zhang, “Understanding masked image modeling via learning occlusion invariant feature,” in IEEE Conf. Comput. Vis. Pattern Recognit
2023
Closest in time.
Z. Huang, X. Jin, C. Lu, Q. Hou, M.-M. Cheng, D. Fu, X. Shen, and J. Feng, “Contrastive masked autoencoders are stronger vision learners,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2023
Closest in time.
C. Tao, X. Zhu, W. Su, G. Huang, B. Li, J. Zhou, Y. Qiao, X. Wang, and J. Dai, “Siamese image modeling for self-supervised vision representation learning,” in Proceedings of the IEEE Conf. Comput. Vis. Pattern Recognit
2023
Closest in time.
Z. Xie, Z. Geng, J. Hu, Z. Zhang, H. Hu, and Y. Cao, “Revealing the dark secrets of masked image modeling,” in IEEE Conf. Comput. Vis. Pattern Recognit
2023
Closest in time.
Q. Garrido, R. Balestriero, L. Najman, and Y. Lecun, “Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,” in Int. Conf. Mach. Learn
2023
Closest in time.
Q. Garrido, Y. Chen, A. Bardes, L. Najman, and Y. LeCun, “On the duality between contrastive and non-contrastive self-supervised learning,” in Int. Conf. Learn. Represent
2023
Closest in time.
S. Lavoie, C. Tsirigotis, M. Schwarzer, A. Vani, M. Noukhovitch, K. Kawaguchi, and A. Courville, “Simplicial embeddings in self-supervised learning and downstream classification,” in Int. Conf. Learn. Represent
2023
Closest in time.