14.5.5 Self-Supervised Learning for Detection

Chapter Contents (Back)
Self-Supervised. Learning. Object Detection.

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


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

Wang, X.L.[Xin-Long], Zhang, R.F.[Ru-Feng], Shen, C.H.[Chun-Hua], Kong, T.[Tao], Li, L.[Lei],
Dense Contrastive Learning for Self-Supervised Visual Pre-Training,
CVPR21(3023-3032)
IEEE DOI 2111
Learning systems, Image segmentation, Visualization, Computational modeling, Semantics, Object detection 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 .


Last update:Jan 13, 2022 at 22:02:22