Rodrigues, F.[Filipe],
Pereira, F.C.[Francisco C.],
Ribeiro, B.[Bernardete],
Learning from multiple annotators:
Distinguishing good from random labelers,
PRL(34), No. 12, 1 September 2013, pp. 1428-1436.
Elsevier DOI
1306
Multiple annotators; Crowdsourcing; Latent variable models;
Expectation-Maximization; Logistic Regression
BibRef
Kleiman, Y.[Yanir],
Goldberg, G.[George],
Amsterdamer, Y.[Yael],
Cohen-Or, D.[Daniel],
Toward semantic image similarity from crowdsourced clustering,
VC(32), No. 6-8, June 2016, pp. 1045-1055.
WWW Link.
1608
BibRef
Martinho-Corbishley, D.[Daniel],
Nixon, M.S.[Mark S.],
Carter, J.N.[John N.],
Analysing comparative soft biometrics from crowdsourced annotations,
IET-Bio(5), No. 4, 2016, pp. 276-283.
DOI Link
1612
BibRef
And:
Retrieving relative soft biometrics for semantic identification,
ICPR16(3067-3072)
IEEE DOI
1705
Biometrics (access control), Cameras, Machine learning,
Neural networks, Semantics, Surveillance, Training
BibRef
Martinho-Corbishley, D.[Daniel],
Nixon, M.S.[Mark S.],
Carter, J.N.[John N.],
Super-Fine Attributes with Crowd Prototyping,
PAMI(41), No. 6, June 2019, pp. 1486-1500.
IEEE DOI
1905
Prototypes, Visualization, Face, Surveillance, Face recognition,
Attribute-based pedestrian re-identification, soft biometrics,
PETA dataset
BibRef
Maharjan, S.,
Zhang, Y.,
Gjessing, S.,
Optimal Incentive Design for Cloud-Enabled Multimedia Crowdsourcing,
MultMed(18), No. 12, December 2016, pp. 2470-2481.
IEEE DOI
1612
Cloud computing
BibRef
Kovashka, A.[Adriana],
Russakovsky, O.[Olga],
Fei-Fei, L.[Li],
Grauman, K.[Kristen],
Crowdsourcing in Computer Vision,
FTCGV(10), No. 3, 2016, pp. 177-243.
DOI Link
1612
Crowdsourcing.
BibRef
Rodrigues, F.[Filipe],
Lourenço, M.,
Ribeiro, B.[Bernardete],
Pereira, F.C.[Francisco C.],
Learning Supervised Topic Models for Classification and Regression
from Crowds,
PAMI(39), No. 12, December 2017, pp. 2409-2422.
IEEE DOI
1711
Analytical models, Data models, Inference algorithms,
Predictive models, Stochastic processes,
Topic models, crowdsoucing.
BibRef
Krishna, R.[Ranjay],
Zhu, Y.[Yuke],
Groth, O.[Oliver],
Johnson, J.[Justin],
Hata, K.[Kenji],
Kravitz, J.[Joshua],
Chen, S.[Stephanie],
Kalantidis, Y.[Yannis],
Li, L.J.[Li-Jia],
Shamma, D.A.[David A.],
Bernstein, M.S.[Michael S.],
Fei-Fei, L.[Li],
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense
Image Annotations,
IJCV(123), No. 1, May 2017, pp. 32-73.
Springer DOI
1705
BibRef
Kumar, G.[Gautam],
Narducci, F.[Fabio],
Bakshi, S.[Sambit],
Knowledge Transfer and Crowdsourcing in Cyber-Physical-Social Systems,
PRL(164), 2022, pp. 210-215.
Elsevier DOI
2212
Cyber-physical-social systems, IoT, Crowdsourcing, Knowledge transfer
BibRef
Zhang, J.[Jing],
Xu, S.[Sunyue],
Sheng, V.S.[Victor S.],
Crowdmeta: Crowdsourcing truth inference with meta-Knowledge transfer,
PR(140), 2023, pp. 109525.
Elsevier DOI
2305
Crowdsourcing, Truth inference, Transfer learning, Meta learning
BibRef
Kim, K.H.,
Aodha, O.M.,
Perona, P.,
Context Embedding Networks,
CVPR18(8679-8687)
IEEE DOI
1812
Visualization, Context modeling, Feature extraction, Training,
Noise measurement, Data models, Crowdsourcing
BibRef
Zhuang, B.[Bohan],
Liu, L.Q.[Ling-Qiao],
Li, Y.[Yao],
Shen, C.H.[Chun-Hua],
Reid, I.D.[Ian D.],
Attend in Groups: A Weakly-Supervised Deep Learning Framework for
Learning from Web Data,
CVPR17(2915-2924)
IEEE DOI
1711
Crowdsource.
Convolution, Feature extraction, Machine learning,
Noise measurement, Robustness, Training, Visualization
BibRef
Sharmanska, V.,
Hernandez-Lobato, D.,
Hernandez-Lobato, J.M.,
Quadrianto, N.,
Ambiguity Helps:
Classification with Disagreements in Crowdsourced Annotations,
CVPR16(2194-2202)
IEEE DOI
1612
BibRef
Nicholson, B.[Bryce],
Sheng, V.S.[Victor S.],
Zhang, J.[Jing],
Noise correction of image labeling in crowdsourcing,
ICIP15(1458-1462)
IEEE DOI
1512
BibRef
Raykar, V.C.,
Yu, S.P.[Shi-Peng],
An Entropic Score to Rank Annotators for Crowdsourced Labeling Tasks,
NCVPRIPG11(29-32).
IEEE DOI
1205
BibRef
Welinder, P.[Peter],
Perona, P.[Pietro],
Online crowdsourcing:
Rating annotators and obtaining cost-effective labels,
ACVHL10(25-32).
IEEE DOI
1006
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
ACRONYM and SUCCESSOR Papers - Stanford University and Others .