Tian, Q.[Qi],
Wu, Y.[Ying],
Yu, J.[Jie],
Huang, T.S.[Thomas S.],
Self-supervised learning based on discriminative nonlinear features for
image classification,
PR(38), No. 6, June 2005, pp. 903-917.
Elsevier DOI
0501
BibRef
Wu, Y.[Ying],
Huang, T.S.[Thomas S.],
Toyama, K.[Kentaro],
Self-Supervised Learning for Object Recognition based on Kernel
Discriminant-EM Algorithm,
ICCV01(I: 275-280).
IEEE DOI
0106
BibRef
Zeng, Z.[Zeng],
Xulei, Y.[Yang],
Qiyun, Y.[Yu],
Meng, Y.[Yao],
Le, Z.[Zhang],
SeSe-Net: Self-Supervised deep learning for segmentation,
PRL(128), 2019, pp. 23-29.
Elsevier DOI
1912
Self-Supervised learning, Deep learning, Segmentation, U-Net
BibRef
You, C.[Chong],
Li, C.[Chi],
Robinson, D.P.[Daniel P.],
Vidal, R.[René],
Self-Representation Based Unsupervised Exemplar Selection in a Union
of Subspaces,
PAMI(44), No. 5, May 2022, pp. 2698-2711.
IEEE DOI
2204
Clustering algorithms, Data models, Databases, Clustering methods,
Optimization, Image reconstruction,
subspace clustering
BibRef
Shi, W.J.[Wen-Jie],
Huang, G.[Gao],
Song, S.[Shiji],
Wang, Z.Y.[Zhuo-Yuan],
Lin, T.[Tingyu],
Wu, C.[Cheng],
Self-Supervised Discovering of Interpretable Features for
Reinforcement Learning,
PAMI(44), No. 5, May 2022, pp. 2712-2724.
IEEE DOI
2204
Task analysis, Decision making, Perturbation methods,
Reinforcement learning, Jacobian matrices, Visualization, Games,
decision-making process
BibRef
Durrant, A.[Aiden],
Leontidis, G.[Georgios],
Hyperspherically regularized networks for self-supervision,
IVC(124), 2022, pp. 104494.
Elsevier DOI
2208
Self-supervised learning, Representation learning,
Representation separability, Image classification
BibRef
Hénaff, O.J.[Olivier J.],
Koppula, S.[Skanda],
Alayrac, J.B.[Jean-Baptiste],
van den Oord, A.[Aaron],
Vinyals, O.[Oriol],
Carreira, J.[João],
Efficient Visual Pretraining with Contrastive Detection,
ICCV21(10066-10076)
IEEE DOI
2203
Visualization, Transfer learning, Performance gain,
Feature extraction, Data models, Computational efficiency,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhou, H.Y.[Hong-Yu],
Lu, C.X.[Chi-Xiang],
Yang, S.[Sibei],
Han, X.G.[Xiao-Guang],
Yu, Y.Z.[Yi-Zhou],
Preservational Learning Improves Self-supervised Medical Image Models
by Reconstructing Diverse Contexts,
ICCV21(3479-3489)
IEEE DOI
2203
WWW Link. Representation learning, Protocols, Codes, Computational modeling,
Estimation, Task analysis, Medical, biological, and cell microscopy,
BibRef
Jiang, Y.F.[Yi-Fan],
Zhang, H.[He],
Zhang, J.M.[Jian-Ming],
Wang, Y.[Yilin],
Lin, Z.[Zhe],
Sunkavalli, K.[Kalyan],
Chen, S.[Simon],
Amirghodsi, S.[Sohrab],
Kong, S.[Sarah],
Wang, Z.Y.[Zhang-Yang],
SSH: A Self-Supervised Framework for Image Harmonization,
ICCV21(4812-4821)
IEEE DOI
2203
Measurement, Training, Visualization, Image color analysis,
Perturbation methods, Training data,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Huang, S.Y.[Si-Yuan],
Xie, Y.C.[Yi-Chen],
Zhu, S.C.[Song-Chun],
Zhu, Y.X.[Yi-Xin],
Spatio-temporal Self-Supervised Representation Learning for 3D Point
Clouds,
ICCV21(6515-6525)
IEEE DOI
2203
Point cloud compression, Representation learning, Training,
Solid modeling, Visualization, Supervised learning, Stereo,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Hu, K.[Kai],
Shao, J.[Jie],
Liu, Y.[Yuan],
Raj, B.[Bhiksha],
Savvides, M.[Marios],
Shen, Z.Q.[Zhi-Qiang],
Contrast and Order Representations for Video Self-Supervised Learning,
ICCV21(7919-7929)
IEEE DOI
2203
Representation learning, Computational modeling,
Predictive models, Task analysis, Videos, Representation learning
BibRef
Qian, R.[Rui],
Li, Y.X.[Yu-Xi],
Liu, H.B.[Hua-Bin],
See, J.[John],
Ding, S.R.[Shuang-Rui],
Liu, X.[Xian],
Li, D.[Dian],
Lin, W.Y.[Wei-Yao],
Enhancing Self-supervised Video Representation Learning via
Multi-level Feature Optimization,
ICCV21(7970-7981)
IEEE DOI
2203
Representation learning, Codes, Computational modeling, Semantics,
Reliability, Task analysis, Video analysis and understanding,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Huang, D.[Deng],
Wu, W.H.[Wen-Hao],
Hu, W.[Weiwen],
Liu, X.[Xu],
He, D.L.[Dong-Liang],
Wu, Z.H.[Zhi-Hua],
Wu, X.[Xiangmiao],
Tan, M.[Mingkui],
Ding, E.[Errui],
ASCNet: Self-supervised Video Representation Learning with
Appearance-Speed Consistency,
ICCV21(8076-8085)
IEEE DOI
2203
Representation learning, Visualization, Image recognition, Codes,
Noise measurement, Data mining, Video analysis and understanding,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Kim, D.H.[Dong-Hyun],
Saito, K.[Kuniaki],
Oh, T.H.[Tae-Hyun],
Plummer, B.A.[Bryan A.],
Sclaroff, S.[Stan],
Saenko, K.[Kate],
CDS: Cross-Domain Self-supervised Pre-training,
ICCV21(9103-9112)
IEEE DOI
2203
Transfer learning, Task analysis, Standards,
Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning
BibRef
Wang, G.[Guangrun],
Wang, K.[Keze],
Wang, G.[Guangcong],
Torr, P.H.S.[Philip H.S.],
Lin, L.[Liang],
Solving Inefficiency of Self-supervised Representation Learning,
ICCV21(9485-9495)
IEEE DOI
2203
Training, Representation learning, Computational modeling,
Supervised learning, Benchmark testing, Task analysis,
Representation learning
BibRef
Patrick, M.[Mandela],
Asano, Y.M.[Yuki M.],
Kuznetsova, P.[Polina],
Fong, R.[Ruth],
Henriques, J.F.[João F.],
Zweig, G.[Geoffrey],
Vedaldi, A.[Andrea],
On Compositions of Transformations in Contrastive Self-Supervised
Learning,
ICCV21(9557-9567)
IEEE DOI
2203
Codes, Benchmark testing, Encoding, Standards, Videos,
Representation learning, Vision + other modalities
BibRef
Hua, T.[Tianyu],
Wang, W.X.[Wen-Xiao],
Xue, Z.[Zihui],
Ren, S.[Sucheng],
Wang, Y.[Yue],
Zhao, H.[Hang],
On Feature Decorrelation in Self-Supervised Learning,
ICCV21(9578-9588)
IEEE DOI
2203
Representation learning, Correlation, Robustness, Decorrelation,
Covariance matrices, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Kotar, K.[Klemen],
Ilharco, G.[Gabriel],
Schmidt, L.[Ludwig],
Ehsani, K.[Kiana],
Mottaghi, R.[Roozbeh],
Contrasting Contrastive Self-Supervised Representation Learning
Pipelines,
ICCV21(9929-9939)
IEEE DOI
2203
Representation learning, Training, Visualization, Pipelines,
Benchmark testing, Data models, Representation learning,
BibRef
Tian, Y.L.[Yong-Long],
Hénaff, O.J.[Olivier J.],
van den Oord, A.[Aäron],
Divide and Contrast: Self-supervised Learning from Uncurated Data,
ICCV21(10043-10054)
IEEE DOI
2203
Annotations, Benchmark testing, Data mining, Task analysis,
Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhao, Y.C.[Yu-Cheng],
Wang, G.[Guangting],
Luo, C.[Chong],
Zeng, W.J.[Wen-Jun],
Zha, Z.J.[Zheng-Jun],
Self-Supervised Visual Representations Learning by Contrastive Mask
Prediction,
ICCV21(10140-10149)
IEEE DOI
2203
Training, Representation learning, Visualization, Head, Semantics,
Performance gain, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Ayush, K.[Kumar],
Uzkent, B.[Burak],
Meng, C.L.[Chen-Lin],
Tanmay, K.[Kumar],
Burke, M.[Marshall],
Lobell, D.[David],
Ermon, S.[Stefano],
Geography-Aware Self-Supervised Learning,
ICCV21(10161-10170)
IEEE DOI
2203
Training, Image segmentation, Supervised learning, Semantics,
Object detection, Task analysis, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Xiong, Y.[Yuwen],
Ren, M.[Mengye],
Zeng, W.Y.[Wen-Yuan],
Waabi, R.U.[Raquel Urtasun],
Self-Supervised Representation Learning from Flow Equivariance,
ICCV21(10171-10180)
IEEE DOI
2203
Representation learning, Image segmentation, Semantics, Crops,
Object detection, Streaming media, Representation learning,
Vision for robotics and autonomous vehicles
BibRef
Zhang, Z.[Zaiwei],
Girdhar, R.[Rohit],
Joulin, A.[Armand],
Misra, I.[Ishan],
Self-Supervised Pretraining of 3D Features on any Point-Cloud,
ICCV21(10232-10243)
IEEE DOI
2203
Training, Solid modeling, Image recognition, Object detection,
Computer architecture, Representation learning,
3D from multiview and other sensors
BibRef
Koohpayegani, S.A.[Soroush Abbasi],
Tejankar, A.[Ajinkya],
Pirsiavash, H.[Hamed],
Mean Shift for Self-Supervised Learning,
ICCV21(10306-10315)
IEEE DOI
2203
Codes, Clustering algorithms, Task analysis,
Residual neural networks, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Gavrilyuk, K.[Kirill],
Jain, M.[Mihir],
Karmanov, I.[Ilia],
Snoek, C.G.M.[Cees G. M.],
Motion-Augmented Self-Training for Video Recognition at Smaller Scale,
ICCV21(10409-10418)
IEEE DOI
2203
Training, Optical losses, Knowledge engineering,
Computational modeling, Semisupervised learning,
Action and behavior recognition
BibRef
Pantazis, O.[Omiros],
Brostow, G.J.[Gabriel J.],
Jones, K.E.[Kate E.],
Aodha, O.M.[Oisin Mac],
Focus on the Positives:
Self-Supervised Learning for Biodiversity Monitoring,
ICCV21(10563-10572)
IEEE DOI
2203
Training, Visualization, Transfer learning, Benchmark testing,
Cameras, Biodiversity, Representation learning, Medical, biological,
Recognition and classification
BibRef
Zhang, L.Z.[Ling-Zhi],
Du, W.Y.[Wei-Yu],
Zhou, S.H.[Sheng-Hao],
Wang, J.C.[Jian-Cong],
Shi, J.B.[Jian-Bo],
Inpaint2Learn: A Self-Supervised Framework for Affordance Learning,
WACV22(3778-3787)
IEEE DOI
2202
Training, Affordances, Pipelines, Predictive models,
Benchmark testing, Adversarial machine learning,
Analysis and Understanding Scene Understanding
BibRef
Mazumder, P.[Pratik],
Singh, P.[Pravendra],
Namboodiri, V.P.[Vinay P.],
Fair Visual Recognition in Limited Data Regime using Self-Supervision
and Self-Distillation,
WACV22(3889-3897)
IEEE DOI
2202
Training, Deep learning, Adaptation models, Visualization,
Computational modeling, Training data,
Privacy and Ethics in Vision
BibRef
Zheltonozhskii, E.[Evgenii],
Baskin, C.[Chaim],
Mendelson, A.[Avi],
Bronstein, A.M.[Alex M.],
Litany, O.[Or],
Contrast to Divide: Self-Supervised Pre-Training for Learning with
Noisy Labels,
WACV22(387-397)
IEEE DOI
2202
Training, Upper bound, Neural networks,
Semisupervised learning, Feature extraction, Robustness, Transfer,
Large-scale Vision Applications
BibRef
Reed, C.J.[Colorado J.],
Yue, X.Y.[Xiang-Yu],
Nrusimha, A.[Ani],
Ebrahimi, S.[Sayna],
Vijaykumar, V.[Vivek],
Mao, R.[Richard],
Li, B.[Bo],
Zhang, S.H.[Shang-Hang],
Guillory, D.[Devin],
Metzger, S.[Sean],
Keutzer, K.[Kurt],
Darrell, T.J.[Trevor J.],
Self-Supervised Pretraining Improves Self-Supervised Pretraining,
WACV22(1050-1060)
IEEE DOI
2202
Computational modeling, Data models, Robustness,
Task analysis, X-ray imaging, Convergence, Transfer, Few-shot,
Vision for Aerial/Drone/Underwater/Ground Vehicles
BibRef
Zhang, Z.[Zehua],
Crandall, D.[David],
Hierarchically Decoupled Spatial-Temporal Contrast for
Self-supervised Video Representation Learning,
WACV22(975-985)
IEEE DOI
2202
Representation learning, Deep learning, Codes,
Semantics, Supervised learning, Benchmark testing, Transfer,
Analysis and Understanding
BibRef
Huynh, T.[Tri],
Kornblith, S.[Simon],
Walter, M.R.[Matthew R.],
Maire, M.[Michael],
Khademi, M.[Maryam],
Boosting Contrastive Self-Supervised Learning with False Negative
Cancellation,
WACV22(986-996)
IEEE DOI
2202
Representation learning, Visualization, Codes,
Computational modeling, Semantics, Boosting, Transfer, Few-shot,
Semi- and Un- supervised Learning Deep Learning
BibRef
Yamaguchi, S.[Shin'ya],
Kanai, S.[Sekitoshi],
Shioda, T.[Tetsuya],
Takeda, S.[Shoichiro],
Image Enhanced Rotation Prediction for Self-Supervised Learning,
ICIP21(489-493)
IEEE DOI
2201
Shape, Predictive models, Network architecture, Benchmark testing,
Task analysis, Image enhancement, Self-supervised learning, CNN
BibRef
Chen, T.L.[Tian-Long],
Frankle, J.[Jonathan],
Chang, S.[Shiyu],
Liu, S.[Sijia],
Zhang, Y.[Yang],
Carbin, M.[Michael],
Wang, Z.Y.[Zhang-Yang],
The Lottery Tickets Hypothesis for Supervised and Self-supervised
Pre-training in Computer Vision Models,
CVPR21(16301-16311)
IEEE DOI
2111
Degradation, Image segmentation, Sensitivity,
Computational modeling, Perturbation methods, Pattern recognition
BibRef
Selvaraju, R.R.[Ramprasaath R.],
Desai, K.[Karan],
Johnson, J.[Justin],
Naik, N.[Nikhil],
CASTing Your Model:
Learning to Localize Improves Self-Supervised Representations,
CVPR21(11053-11062)
IEEE DOI
2111
Visualization, Correlation, Codes, Grounding, Crops, Robustness
BibRef
Hou, L.[Luwei],
Zhang, Y.[Yu],
Fu, K.[Kui],
Li, J.[Jia],
Informative and Consistent Correspondence Mining for Cross-Domain
Weakly Supervised Object Detection,
CVPR21(9924-9933)
IEEE DOI
2111
Annotations, Collaboration, Object detection,
Detectors, Generators, Pattern recognition
BibRef
Yang, C.[Ceyuan],
Wu, Z.R.[Zhi-Rong],
Zhou, B.[Bolei],
Lin, S.[Stephen],
Instance Localization for Self-supervised Detection Pretraining,
CVPR21(3986-3995)
IEEE DOI
2111
Location awareness, Transfer learning, Semantics,
Object detection, Computer architecture, Pattern recognition
BibRef
Ericsson, L.[Linus],
Gouk, H.[Henry],
Hospedales, T.M.[Timothy M.],
How Well Do Self-Supervised Models Transfer?,
CVPR21(5410-5419)
IEEE DOI
2111
Visualization, Image recognition,
Image color analysis, Computational modeling, Object detection,
Predictive models
BibRef
Tang, Y.H.[Yi-He],
Chen, W.F.[Wei-Feng],
Luo, Y.J.[Yi-Jun],
Zhang, Y.T.[Yu-Ting],
Humble Teachers Teach Better Students for Semi-Supervised Object
Detection,
CVPR21(3131-3140)
IEEE DOI
2111
Training, Object detection, Detectors,
Benchmark testing, Feature extraction, Data models
BibRef
Gudovskiy, D.,
Hodgkinson, A.,
Yamaguchi, T.,
Tsukizawa, S.,
Deep Active Learning for Biased Datasets via Fisher Kernel
Self-Supervision,
CVPR20(9038-9046)
IEEE DOI
2008
Task analysis, Training, Kernel, Labeling, Artificial intelligence,
Data models, Training data
BibRef
Li, Y.D.[Yan-Dong],
Huang, D.[Di],
Qin, D.F.[Dan-Feng],
Wang, L.Q.[Li-Qiang],
Gong, B.Q.[Bo-Qing],
Improving Object Detection with Selective Self-supervised Self-training,
ECCV20(XXIX: 589-607).
Springer DOI
2010
BibRef
Lee, W.[Wonhee],
Na, J.[Joonil],
Kim, G.[Gunhee],
Multi-Task Self-Supervised Object Detection via Recycling of Bounding
Box Annotations,
CVPR19(4979-4988).
IEEE DOI
2002
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Instance Learning .