13.6.1.2 Crowdsourcing, Recognition, Analysis, Descriptions

Chapter Contents (Back)
Knowledge. Crowdsourcing.

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., Nixon, M.S., Carter, J.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

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], Lourenšo, 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


Horn, G.V., Branson, S., Loarie, S., Belongie, S., Perona, P.,
Lean Multiclass Crowdsourcing,
CVPR18(2714-2723)
IEEE DOI 1812
Task analysis, Computational modeling, Crowdsourcing, Taxonomy, Predictive models, Computer vision, Birds 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 .


Last update:Mar 2, 2019 at 12:07:42