Hernández-González, J.[Jerónimo],
Inza, I.[Iñaki],
Lozano, J.A.[Jose A.],
Weak supervision and other non-standard classification problems:
A taxonomy,
PRL(69), No. 1, 2016, pp. 49-55.
Elsevier DOI
1601
Weakly supervised classification
BibRef
Xu, L.,
Clausi, D.A.,
Li, F.,
Wong, A.,
Weakly Supervised Classification of Remotely Sensed Imagery Using
Label Constraint and Edge Penalty,
GeoRS(55), No. 3, March 2017, pp. 1424-1436.
IEEE DOI
1703
Correlation
BibRef
Jiao, Q.[Qihan],
Liu, Z.[Zhi],
Ye, L.W.[Lin-Wei],
Wang, Y.[Yang],
Weakly labeled fine-grained classification with hierarchy
relationship of fine and coarse labels,
JVCIR(63), 2019, pp. 102584.
Elsevier DOI
1909
Fine-grained classification, Weakly labeled images,
Convolutional block attention, Feature fusion
BibRef
Lei, J.[Jie],
Guo, Z.Y.[Zhen-Yu],
Wang, Y.[Yang],
Weakly Supervised Image Classification with Coarse and Fine Labels,
CRV17(240-247)
IEEE DOI
1804
convolution, feedforward neural nets, image classification,
coarse labels, convolutional neural networks,
weakly supervised
BibRef
Li, Y.F.[Yu-Feng],
Guo, L.Z.[Lan-Zhe],
Zhou, Z.H.[Zhi-Hua],
Towards Safe Weakly Supervised Learning,
PAMI(43), No. 1, January 2021, pp. 334-346.
IEEE DOI
2012
Supervised learning, Task analysis, Performance gain,
Semisupervised learning, Machine learning, Proposals,
label noise learning
BibRef
Tao, H.[Hong],
Jiang, J.C.[Jia-Cheng],
Hou, C.P.[Chen-Ping],
Luo, T.J.[Ting-Jin],
Fan, R.D.[Rui-Dong],
Zhang, J.[Jing],
Compound Weakly Supervised Clustering,
IP(33), 2024, pp. 957-971.
IEEE DOI
2402
Face recognition, Compounds, Image reconstruction,
Clustering methods, Clustering algorithms, Task analysis,
incomplete view learning
BibRef
Yuan, S.[Suqin],
Li, M.[Muyang],
Feng, L.[Lei],
Han, B.[Bo],
Niu, G.[Gang],
Sugiyama, M.[Masashi],
Liu, T.L.[Tong-Liang],
Early Stopping Without Validation Data in Weakly Supervised Learning,
PAMI(48), No. 11, November 2026, pp. 14071-14086.
IEEE DOI
2610
Labeling, Modeling, Training, Noise, Waves,
Wireless Access in Vehicular Environments, Noise measurement,
trustworthy machine learning
BibRef
Tian, S.Y.[Shi-Yu],
Wei, H.X.[Hong-Xin],
Wang, Y.Q.[Yi-Qun],
Feng, L.[Lei],
CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label
Learning,
CVPR24(19479-19488)
IEEE DOI
2410
Training, Computational modeling, Noise, Supervised learning,
Benchmark testing, Predictive models, Partial-label learning,
Weakly supervised learning
BibRef
Wang, Y.T.[Yu-Ting],
Ilic, V.[Velibor],
Li, J.[Jiatong],
Kisacanin, B.[Branislav],
Pavlovic, V.[Vladimir],
ALWOD: Active Learning for Weakly-Supervised Object Detection,
ICCV23(6436-6446)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zhu, J.G.[Jia-Geng],
Xie, H.C.[Han-Chen],
Abd-Almageed, W.[Wael],
Weakly Supervised Invariant Representation Learning via Disentangling
Known and Unknown Nuisance Factors,
Scarce22(382-395).
Springer DOI
2304
BibRef
Gao, M.F.[Ming-Fei],
Li, A.[Ang],
Yu, R.C.[Rui-Chi],
Morariu, V.I.[Vlad I.],
Davis, L.S.[Larry S.],
C-WSL: Count-Guided Weakly Supervised Localization,
ECCV18(I: 155-171).
Springer DOI
1810
BibRef
Mahajan, D.[Dhruv],
Girshick, R.[Ross],
Ramanathan, V.[Vignesh],
He, K.M.[Kai-Ming],
Paluri, M.[Manohar],
Li, Y.X.[Yi-Xuan],
Bharambe, A.[Ashwin],
van der Maaten, L.[Laurens],
Exploring the Limits of Weakly Supervised Pretraining,
ECCV18(II: 185-201).
Springer DOI
1810
BibRef
Gaur, U.,
Manjunath, B.S.,
Weakly Supervised Manifold Learning for Dense Semantic Object
Correspondence,
ICCV17(1744-1752)
IEEE DOI
1802
feature extraction, feedforward neural nets, geometry,
image classification, learning (artificial intelligence), Semantics
BibRef
Joulin, A.[Armand],
van der Maaten, L.[Laurens],
Jabri, A.[Allan],
Vasilache, N.[Nicolas],
Learning Visual Features from Large Weakly Supervised Data,
ECCV16(VII: 67-84).
Springer DOI
1611
BibRef
Hoai, M.[Minh],
Zisserman, A.[Andrew],
Discriminative Sub-categorization,
CVPR13(1666-1673)
IEEE DOI
1309
Weakly supervised with positive and negative examples.
BibRef
Zhang, T.Z.[Tian-Zhu],
Cai, R.[Rui],
Li, Z.W.[Zhi-Wei],
Zhang, L.[Lei],
Lu, H.Q.[Han-Qing],
Hierarchical Object Representations for Visual Recognition via Weakly
Supervised Learning,
ACCV12(I:474-485).
Springer DOI
1304
BibRef
Siva, P.[Parthipan],
Russell, C.[Chris],
Xiang, T.[Tao],
In Defence of Negative Mining for Annotating Weakly Labelled Data,
ECCV12(III: 594-608).
Springer DOI
1210
Annotate based on examples that have not occurred previously.
BibRef
Bordes, J.B.,
Prinet, V.,
Mixture Distributions for Weakly Supervised Classification in Remote
Sensing Images,
BMVC08(xx-yy).
PDF File.
0809
BibRef
Pantofaru, C.,
Hebert, M.,
A framework for learning to recognize and segment object classes using
weakly supervised training data,
BMVC07(xx-yy).
PDF File.
0709
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Semi-Supervised Clustering Applied to Hyperspectral Data .